ScanNet200 3D Semantic Label Benchmark
The 3D semantic labeling task involves predicting a semantic labeling of a 3D scan mesh.
Evaluation and metricsOur evaluation ranks all methods according to the PASCAL VOC intersection-over-union metric (IoU). IoU = TP/(TP+FP+FN), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively. Predicted labels are evaluated per-vertex over the respective 3D scan mesh; for 3D approaches that operate on other representations like grids or points, the predicted labels should be mapped onto the mesh vertices (e.g., one such example for grid to mesh vertices is provided in the evaluation helpers).
This table lists the benchmark results for the ScanNet200 3D semantic label scenario.
Method | Info | avg iou | head iou | common iou | tail iou | wall | chair | floor | table | door | couch | cabinet | shelf | desk | office chair | bed | pillow | sink | picture | window | toilet | bookshelf | monitor | curtain | book | armchair | coffee table | box | refrigerator | lamp | kitchen cabinet | towel | clothes | tv | nightstand | counter | dresser | stool | cushion | plant | ceiling | bathtub | end table | dining table | keyboard | bag | backpack | toilet paper | printer | tv stand | whiteboard | blanket | shower curtain | trash can | closet | stairs | microwave | stove | shoe | computer tower | bottle | bin | ottoman | bench | board | washing machine | mirror | copier | basket | sofa chair | file cabinet | fan | laptop | shower | paper | person | paper towel dispenser | oven | blinds | rack | plate | blackboard | piano | suitcase | rail | radiator | recycling bin | container | wardrobe | soap dispenser | telephone | bucket | clock | stand | light | laundry basket | pipe | clothes dryer | guitar | toilet paper holder | seat | speaker | column | bicycle | ladder | bathroom stall | shower wall | cup | jacket | storage bin | coffee maker | dishwasher | paper towel roll | machine | mat | windowsill | bar | toaster | bulletin board | ironing board | fireplace | soap dish | kitchen counter | doorframe | toilet paper dispenser | mini fridge | fire extinguisher | ball | hat | shower curtain rod | water cooler | paper cutter | tray | shower door | pillar | ledge | toaster oven | mouse | toilet seat cover dispenser | furniture | cart | storage container | scale | tissue box | light switch | crate | power outlet | decoration | sign | projector | closet door | vacuum cleaner | candle | plunger | stuffed animal | headphones | dish rack | broom | guitar case | range hood | dustpan | hair dryer | water bottle | handicap bar | purse | vent | shower floor | water pitcher | mailbox | bowl | paper bag | alarm clock | music stand | projector screen | divider | laundry detergent | bathroom counter | object | bathroom vanity | closet wall | laundry hamper | bathroom stall door | ceiling light | trash bin | dumbbell | stair rail | tube | bathroom cabinet | cd case | closet rod | coffee kettle | structure | shower head | keyboard piano | case of water bottles | coat rack | storage organizer | folded chair | fire alarm | power strip | calendar | poster | potted plant | luggage | mattress |
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AWCS | 0.305 14 | 0.508 14 | 0.225 14 | 0.142 11 | 0.782 14 | 0.634 17 | 0.937 14 | 0.489 15 | 0.578 14 | 0.721 12 | 0.364 15 | 0.355 11 | 0.515 12 | 0.023 9 | 0.764 14 | 0.523 11 | 0.707 14 | 0.264 11 | 0.633 14 | 0.922 14 | 0.507 13 | 0.886 1 | 0.804 15 | 0.179 15 | 0.436 16 | 0.300 12 | 0.656 16 | 0.529 3 | 0.501 15 | 0.394 13 | 0.296 16 | 0.820 5 | 0.603 9 | 0.131 3 | 0.179 17 | 0.619 3 | 0.000 1 | 0.707 16 | 0.865 13 | 0.773 6 | 0.171 7 | 0.010 6 | 0.484 14 | 0.063 13 | 0.463 13 | 0.254 13 | 0.332 16 | 0.649 11 | 0.220 11 | 0.100 11 | 0.729 15 | 0.613 15 | 0.071 13 | 0.582 14 | 0.628 7 | 0.702 4 | 0.424 15 | 0.749 2 | 0.137 15 | 0.000 3 | 0.142 13 | 0.360 13 | 0.863 6 | 0.305 14 | 0.877 10 | 0.000 7 | 0.173 5 | 0.606 12 | 0.337 14 | 0.478 12 | 0.154 15 | 0.000 1 | 0.253 14 | 0.664 8 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.626 13 | 0.782 10 | 0.302 16 | 0.602 7 | 0.185 13 | 0.282 6 | 0.651 13 | 0.317 13 | 0.000 3 | 0.000 7 | 0.000 1 | 0.022 13 | 0.000 4 | 0.154 2 | 0.876 9 | 0.000 1 | 0.014 9 | 0.063 9 | 0.029 17 | 0.553 7 | 0.467 3 | 0.084 13 | 0.124 14 | 0.157 16 | 0.049 12 | 0.373 13 | 0.252 9 | 0.097 15 | 0.000 4 | 0.219 7 | 0.542 3 | 0.000 3 | 0.392 7 | 0.172 8 | 0.000 15 | 0.339 9 | 0.417 8 | 0.533 13 | 0.093 15 | 0.115 10 | 0.195 9 | 0.000 1 | 0.516 10 | 0.288 15 | 0.741 6 | 0.000 1 | 0.001 10 | 0.233 9 | 0.056 14 | 0.000 7 | 0.159 6 | 0.334 7 | 0.077 9 | 0.000 8 | 0.000 11 | 0.000 1 | 0.000 5 | 0.749 13 | 0.000 1 | 0.411 8 | 0.000 1 | 0.008 11 | 0.452 10 | 0.000 1 | 0.595 10 | 0.000 1 | 0.000 1 | 0.220 10 | 0.006 12 | 0.894 12 | 0.006 8 | 0.000 11 | 0.000 1 | 0.000 9 | 0.000 1 | 0.112 6 | 0.504 8 | 0.404 10 | 0.551 3 | 0.093 4 | 0.129 14 | 0.484 10 | 0.381 17 | 0.000 12 | 0.000 1 | 0.396 14 | 0.000 2 | 0.000 2 | 0.620 3 | 0.402 17 | 0.000 3 | 0.000 1 | 0.000 1 | 0.142 9 | 0.000 7 | 0.000 3 | 0.512 9 | 0.000 1 | |||||||||||||||||||||||||||||
: Long-Tailed 3D Semantic Segmentation with Adaptive Weight Constraint and Sampling. ICRA 2024 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
ALS-MinkowskiNet | ![]() | 0.414 2 | 0.610 2 | 0.322 3 | 0.271 2 | 0.852 1 | 0.710 2 | 0.973 1 | 0.572 4 | 0.719 3 | 0.795 2 | 0.477 6 | 0.506 2 | 0.601 3 | 0.000 14 | 0.804 5 | 0.646 3 | 0.804 2 | 0.344 2 | 0.777 1 | 0.984 1 | 0.671 1 | 0.879 2 | 0.936 1 | 0.342 5 | 0.632 7 | 0.449 4 | 0.817 3 | 0.475 10 | 0.723 2 | 0.798 1 | 0.376 8 | 0.832 2 | 0.693 1 | 0.031 9 | 0.564 1 | 0.510 13 | 0.000 1 | 0.893 3 | 0.905 1 | 0.672 16 | 0.314 1 | 0.000 7 | 0.718 1 | 0.153 3 | 0.542 2 | 0.397 3 | 0.726 3 | 0.752 8 | 0.252 8 | 0.226 2 | 0.916 2 | 0.800 1 | 0.047 16 | 0.807 3 | 0.769 1 | 0.709 3 | 0.630 3 | 0.769 1 | 0.217 10 | 0.000 3 | 0.285 1 | 0.598 4 | 0.846 10 | 0.535 1 | 0.956 4 | 0.000 7 | 0.137 11 | 0.784 2 | 0.464 7 | 0.463 13 | 0.230 12 | 0.000 1 | 0.598 3 | 0.662 9 | 0.000 4 | 0.087 2 | 0.000 1 | 0.135 3 | 0.900 2 | 0.780 11 | 0.703 2 | 0.741 1 | 0.571 2 | 0.149 9 | 0.697 7 | 0.646 2 | 0.000 3 | 0.076 2 | 0.000 1 | 0.025 11 | 0.000 4 | 0.106 6 | 0.981 1 | 0.000 1 | 0.043 7 | 0.113 4 | 0.888 2 | 0.248 15 | 0.404 4 | 0.252 6 | 0.314 1 | 0.220 7 | 0.245 2 | 0.466 7 | 0.366 2 | 0.159 2 | 0.000 4 | 0.149 8 | 0.690 2 | 0.000 3 | 0.531 5 | 0.253 3 | 0.285 6 | 0.460 1 | 0.440 5 | 0.813 1 | 0.230 3 | 0.283 6 | 0.159 11 | 0.000 1 | 0.728 1 | 0.666 5 | 0.958 1 | 0.000 1 | 0.021 5 | 0.252 8 | 0.118 5 | 0.000 7 | 0.445 3 | 0.223 10 | 0.285 1 | 0.194 3 | 0.390 2 | 0.000 1 | 0.475 4 | 0.842 7 | 0.000 1 | 0.455 3 | 0.000 1 | 0.250 7 | 0.458 8 | 0.000 1 | 0.865 1 | 0.000 1 | 0.000 1 | 0.635 1 | 0.359 5 | 0.972 1 | 0.087 3 | 0.447 1 | 0.000 1 | 0.000 9 | 0.000 1 | 0.129 2 | 0.532 6 | 0.446 8 | 0.503 5 | 0.071 13 | 0.135 12 | 0.699 4 | 0.717 2 | 0.097 2 | 0.000 1 | 0.665 1 | 0.000 2 | 0.000 2 | 1.000 1 | 0.752 6 | 0.000 3 | 0.000 1 | 0.000 1 | 0.142 9 | 0.200 1 | 0.259 1 | 1.000 1 | 0.000 1 | ||||||||||||||||||||||||||||
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum: ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding. arxiv | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
OA-CNN-L_ScanNet200 | 0.333 11 | 0.558 5 | 0.269 9 | 0.124 13 | 0.821 5 | 0.703 3 | 0.946 6 | 0.569 5 | 0.662 4 | 0.748 9 | 0.487 3 | 0.455 4 | 0.572 7 | 0.000 14 | 0.789 9 | 0.534 9 | 0.736 9 | 0.271 8 | 0.713 4 | 0.949 6 | 0.498 14 | 0.877 3 | 0.860 11 | 0.332 7 | 0.706 1 | 0.474 3 | 0.788 7 | 0.406 13 | 0.637 6 | 0.495 11 | 0.355 11 | 0.805 7 | 0.592 12 | 0.015 13 | 0.396 8 | 0.602 6 | 0.000 1 | 0.799 11 | 0.876 7 | 0.713 13 | 0.276 2 | 0.000 7 | 0.493 13 | 0.080 9 | 0.448 14 | 0.363 5 | 0.661 4 | 0.833 6 | 0.262 6 | 0.125 7 | 0.823 12 | 0.665 9 | 0.076 9 | 0.720 8 | 0.557 10 | 0.637 9 | 0.517 9 | 0.672 10 | 0.227 8 | 0.000 3 | 0.158 12 | 0.496 8 | 0.843 11 | 0.352 10 | 0.835 13 | 0.000 7 | 0.103 14 | 0.711 5 | 0.527 4 | 0.526 6 | 0.320 8 | 0.000 1 | 0.568 6 | 0.625 11 | 0.067 1 | 0.000 9 | 0.000 1 | 0.001 5 | 0.806 6 | 0.836 7 | 0.621 10 | 0.591 8 | 0.373 8 | 0.314 5 | 0.668 10 | 0.398 9 | 0.003 2 | 0.000 7 | 0.000 1 | 0.016 16 | 0.024 2 | 0.043 13 | 0.906 6 | 0.000 1 | 0.052 6 | 0.000 10 | 0.384 12 | 0.330 12 | 0.342 8 | 0.100 12 | 0.223 7 | 0.183 13 | 0.112 7 | 0.476 6 | 0.313 7 | 0.130 9 | 0.196 3 | 0.112 12 | 0.370 11 | 0.000 3 | 0.234 12 | 0.071 9 | 0.160 7 | 0.403 6 | 0.398 13 | 0.492 14 | 0.197 6 | 0.076 13 | 0.272 5 | 0.000 1 | 0.200 16 | 0.560 10 | 0.735 7 | 0.000 1 | 0.000 11 | 0.000 12 | 0.110 8 | 0.002 6 | 0.021 8 | 0.412 5 | 0.000 12 | 0.000 8 | 0.000 11 | 0.000 1 | 0.000 5 | 0.794 11 | 0.000 1 | 0.445 5 | 0.000 1 | 0.022 10 | 0.509 7 | 0.000 1 | 0.517 13 | 0.000 1 | 0.000 1 | 0.001 17 | 0.245 7 | 0.915 5 | 0.024 6 | 0.089 7 | 0.000 1 | 0.262 3 | 0.000 1 | 0.103 11 | 0.524 7 | 0.392 11 | 0.515 4 | 0.013 17 | 0.251 4 | 0.411 13 | 0.662 4 | 0.001 11 | 0.000 1 | 0.473 12 | 0.000 2 | 0.000 2 | 0.150 5 | 0.699 9 | 0.000 3 | 0.000 1 | 0.000 1 | 0.166 6 | 0.000 7 | 0.024 2 | 0.000 11 | 0.000 1 | |||||||||||||||||||||||||||||
PonderV2 ScanNet200 | 0.346 6 | 0.552 8 | 0.270 8 | 0.175 9 | 0.810 7 | 0.682 9 | 0.950 5 | 0.560 7 | 0.641 10 | 0.761 3 | 0.398 13 | 0.357 10 | 0.570 8 | 0.113 2 | 0.804 5 | 0.603 6 | 0.750 7 | 0.283 4 | 0.681 7 | 0.952 5 | 0.548 5 | 0.874 4 | 0.852 13 | 0.290 12 | 0.700 2 | 0.356 11 | 0.792 5 | 0.445 12 | 0.545 13 | 0.436 12 | 0.351 12 | 0.787 10 | 0.611 8 | 0.050 8 | 0.290 14 | 0.519 12 | 0.000 1 | 0.825 10 | 0.888 5 | 0.842 3 | 0.259 3 | 0.100 2 | 0.558 7 | 0.070 12 | 0.497 7 | 0.247 14 | 0.457 11 | 0.889 3 | 0.248 9 | 0.106 10 | 0.817 13 | 0.691 6 | 0.094 7 | 0.729 6 | 0.636 6 | 0.620 12 | 0.503 11 | 0.660 13 | 0.243 7 | 0.000 3 | 0.212 7 | 0.590 5 | 0.860 8 | 0.400 5 | 0.881 9 | 0.000 7 | 0.202 2 | 0.622 10 | 0.408 11 | 0.499 8 | 0.261 10 | 0.000 1 | 0.385 10 | 0.636 10 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.433 16 | 0.843 6 | 0.660 6 | 0.574 12 | 0.481 4 | 0.336 4 | 0.677 9 | 0.486 6 | 0.000 3 | 0.030 3 | 0.000 1 | 0.034 6 | 0.000 4 | 0.080 8 | 0.869 10 | 0.000 1 | 0.000 10 | 0.000 10 | 0.540 10 | 0.727 3 | 0.232 17 | 0.115 11 | 0.186 10 | 0.193 9 | 0.000 14 | 0.403 11 | 0.326 6 | 0.103 14 | 0.000 4 | 0.290 4 | 0.392 9 | 0.000 3 | 0.346 10 | 0.062 10 | 0.424 5 | 0.375 7 | 0.431 6 | 0.667 4 | 0.115 14 | 0.082 12 | 0.239 7 | 0.000 1 | 0.504 12 | 0.606 8 | 0.584 12 | 0.000 1 | 0.002 9 | 0.186 10 | 0.104 10 | 0.000 7 | 0.394 5 | 0.384 6 | 0.083 8 | 0.000 8 | 0.007 9 | 0.000 1 | 0.000 5 | 0.880 4 | 0.000 1 | 0.377 10 | 0.000 1 | 0.263 6 | 0.565 3 | 0.000 1 | 0.608 9 | 0.000 1 | 0.000 1 | 0.304 7 | 0.009 11 | 0.924 2 | 0.000 9 | 0.000 11 | 0.000 1 | 0.000 9 | 0.000 1 | 0.128 3 | 0.584 2 | 0.475 7 | 0.412 8 | 0.076 11 | 0.269 3 | 0.621 6 | 0.509 9 | 0.010 7 | 0.000 1 | 0.491 11 | 0.063 1 | 0.000 2 | 0.472 4 | 0.880 4 | 0.000 3 | 0.000 1 | 0.000 1 | 0.179 5 | 0.125 2 | 0.000 3 | 0.441 10 | 0.000 1 | |||||||||||||||||||||||||||||
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PPT-SpUNet-F.T. | 0.332 12 | 0.556 6 | 0.270 7 | 0.123 14 | 0.816 6 | 0.682 9 | 0.946 6 | 0.549 10 | 0.657 8 | 0.756 5 | 0.459 7 | 0.376 9 | 0.550 11 | 0.001 12 | 0.807 4 | 0.616 4 | 0.727 12 | 0.267 9 | 0.691 5 | 0.942 11 | 0.530 9 | 0.872 5 | 0.874 8 | 0.330 8 | 0.542 14 | 0.374 8 | 0.792 5 | 0.400 14 | 0.673 4 | 0.572 7 | 0.433 2 | 0.793 9 | 0.623 7 | 0.008 16 | 0.351 10 | 0.594 8 | 0.000 1 | 0.783 13 | 0.876 7 | 0.833 4 | 0.213 6 | 0.000 7 | 0.537 8 | 0.091 7 | 0.519 4 | 0.304 8 | 0.620 8 | 0.942 2 | 0.264 5 | 0.124 8 | 0.855 7 | 0.695 5 | 0.086 8 | 0.646 10 | 0.506 16 | 0.658 7 | 0.535 6 | 0.715 4 | 0.314 2 | 0.000 3 | 0.241 4 | 0.608 3 | 0.897 2 | 0.359 8 | 0.858 11 | 0.000 7 | 0.076 17 | 0.611 11 | 0.392 12 | 0.509 7 | 0.378 6 | 0.000 1 | 0.579 4 | 0.565 15 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.755 7 | 0.806 9 | 0.661 4 | 0.572 13 | 0.350 9 | 0.181 7 | 0.660 12 | 0.300 14 | 0.000 3 | 0.000 7 | 0.000 1 | 0.023 12 | 0.000 4 | 0.042 14 | 0.930 4 | 0.000 1 | 0.000 10 | 0.077 7 | 0.584 9 | 0.392 10 | 0.339 9 | 0.185 10 | 0.171 12 | 0.308 2 | 0.006 13 | 0.563 3 | 0.256 8 | 0.150 4 | 0.000 4 | 0.002 16 | 0.345 12 | 0.000 3 | 0.045 14 | 0.197 5 | 0.063 11 | 0.323 11 | 0.453 4 | 0.600 8 | 0.163 11 | 0.037 15 | 0.349 4 | 0.000 1 | 0.672 3 | 0.679 4 | 0.753 5 | 0.000 1 | 0.000 11 | 0.000 12 | 0.117 6 | 0.000 7 | 0.000 10 | 0.291 8 | 0.000 12 | 0.000 8 | 0.039 7 | 0.000 1 | 0.000 5 | 0.899 2 | 0.000 1 | 0.374 11 | 0.000 1 | 0.000 12 | 0.545 5 | 0.000 1 | 0.634 5 | 0.000 1 | 0.000 1 | 0.074 13 | 0.223 8 | 0.914 6 | 0.000 9 | 0.021 9 | 0.000 1 | 0.000 9 | 0.000 1 | 0.112 6 | 0.498 10 | 0.649 1 | 0.383 10 | 0.095 2 | 0.135 12 | 0.449 11 | 0.432 12 | 0.008 9 | 0.000 1 | 0.518 7 | 0.000 2 | 0.000 2 | 0.000 11 | 0.796 5 | 0.000 3 | 0.000 1 | 0.000 1 | 0.138 13 | 0.000 7 | 0.000 3 | 0.000 11 | 0.000 1 | |||||||||||||||||||||||||||||
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DITR | 0.449 1 | 0.629 1 | 0.392 1 | 0.289 1 | 0.851 2 | 0.727 1 | 0.969 4 | 0.600 1 | 0.741 2 | 0.805 1 | 0.519 1 | 0.480 3 | 0.636 1 | 0.014 10 | 0.867 1 | 0.680 1 | 0.849 1 | 0.318 3 | 0.753 2 | 0.982 2 | 0.508 12 | 0.871 6 | 0.934 2 | 0.482 1 | 0.596 11 | 0.551 2 | 0.804 4 | 0.508 6 | 0.729 1 | 0.718 2 | 0.417 4 | 0.886 1 | 0.664 3 | 0.000 17 | 0.500 2 | 0.698 1 | 0.000 1 | 0.913 1 | 0.901 3 | 0.766 7 | 0.113 12 | 0.000 7 | 0.617 5 | 0.168 2 | 0.650 1 | 0.477 1 | 0.826 1 | 0.962 1 | 0.348 3 | 0.300 1 | 0.947 1 | 0.776 2 | 0.160 3 | 0.889 1 | 0.651 5 | 0.720 2 | 0.700 1 | 0.728 3 | 0.317 1 | 0.000 3 | 0.238 5 | 0.664 1 | 0.869 4 | 0.514 2 | 0.998 1 | 0.313 3 | 0.138 10 | 0.815 1 | 0.828 1 | 0.622 2 | 0.421 5 | 0.000 1 | 0.823 1 | 0.817 1 | 0.000 4 | 0.000 9 | 0.000 1 | 0.157 2 | 0.866 3 | 0.991 1 | 0.805 1 | 0.660 4 | 0.571 2 | 0.043 12 | 0.709 6 | 0.642 3 | 0.000 3 | 0.000 7 | 0.000 1 | 0.028 10 | 0.018 3 | 0.134 3 | 0.967 2 | 0.000 1 | 0.150 2 | 0.130 2 | 0.949 1 | 0.855 1 | 0.580 1 | 0.262 5 | 0.314 1 | 0.230 5 | 0.222 4 | 0.498 5 | 0.367 1 | 0.153 3 | 0.869 1 | 0.334 2 | 0.397 8 | 0.000 3 | 0.904 1 | 0.486 2 | 1.000 1 | 0.423 4 | 0.484 1 | 0.632 6 | 0.716 1 | 0.733 2 | 0.862 1 | 0.000 1 | 0.433 14 | 0.710 1 | 0.851 2 | 0.000 1 | 0.034 4 | 0.315 3 | 0.385 1 | 0.000 7 | 0.001 9 | 0.268 9 | 0.066 11 | 0.000 8 | 0.278 4 | 0.000 1 | 0.978 1 | 0.839 8 | 0.000 1 | 0.448 4 | 0.000 1 | 0.579 1 | 0.403 12 | 0.000 1 | 0.647 3 | 0.000 1 | 0.000 1 | 0.411 3 | 0.315 6 | 0.904 7 | 0.420 1 | 0.392 2 | 0.000 1 | 0.091 6 | 0.000 1 | 0.128 3 | 0.564 3 | 0.591 3 | 0.568 2 | 0.079 9 | 0.139 9 | 1.000 1 | 0.714 3 | 0.178 1 | 0.000 1 | 0.606 3 | 0.000 2 | 0.000 2 | 0.148 6 | 0.983 1 | 0.000 3 | 0.000 1 | 0.000 1 | 0.374 2 | 0.000 7 | 0.000 3 | 0.662 4 | 0.000 1 | |||||||||||||||||||||||||||||
Karim Abou Zeid, Kadir Yilmaz, Daan de Geus, Alexander Hermans, David Adrian, Timm Linder, Bastian Leibe: DINO in the Room: Leveraging 2D Foundation Models for 3D Segmentation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CeCo | 0.340 7 | 0.551 9 | 0.247 13 | 0.181 6 | 0.784 13 | 0.661 14 | 0.939 13 | 0.564 6 | 0.624 13 | 0.721 12 | 0.484 5 | 0.429 5 | 0.575 5 | 0.027 8 | 0.774 11 | 0.503 14 | 0.753 5 | 0.242 13 | 0.656 11 | 0.945 9 | 0.534 7 | 0.865 7 | 0.860 11 | 0.177 17 | 0.616 8 | 0.400 5 | 0.818 2 | 0.579 1 | 0.615 11 | 0.367 14 | 0.408 6 | 0.726 15 | 0.633 5 | 0.162 1 | 0.360 9 | 0.619 3 | 0.000 1 | 0.828 9 | 0.873 9 | 0.924 2 | 0.109 13 | 0.083 3 | 0.564 6 | 0.057 15 | 0.475 12 | 0.266 11 | 0.781 2 | 0.767 7 | 0.257 7 | 0.100 11 | 0.825 11 | 0.663 10 | 0.048 15 | 0.620 13 | 0.551 12 | 0.595 13 | 0.532 7 | 0.692 8 | 0.246 6 | 0.000 3 | 0.213 6 | 0.615 2 | 0.861 7 | 0.376 7 | 0.900 8 | 0.000 7 | 0.102 15 | 0.660 8 | 0.321 15 | 0.547 5 | 0.226 13 | 0.000 1 | 0.311 13 | 0.742 5 | 0.011 3 | 0.006 8 | 0.000 1 | 0.000 6 | 0.546 15 | 0.824 8 | 0.345 14 | 0.665 2 | 0.450 6 | 0.435 1 | 0.683 8 | 0.411 8 | 0.338 1 | 0.000 7 | 0.000 1 | 0.030 9 | 0.000 4 | 0.068 9 | 0.892 8 | 0.000 1 | 0.063 5 | 0.000 10 | 0.257 13 | 0.304 13 | 0.387 6 | 0.079 14 | 0.228 6 | 0.190 11 | 0.000 14 | 0.586 1 | 0.347 4 | 0.133 7 | 0.000 4 | 0.037 13 | 0.377 10 | 0.000 3 | 0.384 8 | 0.006 16 | 0.003 13 | 0.421 5 | 0.410 10 | 0.643 5 | 0.171 9 | 0.121 9 | 0.142 12 | 0.000 1 | 0.510 11 | 0.447 11 | 0.474 14 | 0.000 1 | 0.000 11 | 0.286 5 | 0.083 11 | 0.000 7 | 0.000 10 | 0.603 1 | 0.096 7 | 0.063 5 | 0.000 11 | 0.000 1 | 0.000 5 | 0.898 3 | 0.000 1 | 0.429 7 | 0.000 1 | 0.400 2 | 0.550 4 | 0.000 1 | 0.633 6 | 0.000 1 | 0.000 1 | 0.377 5 | 0.000 15 | 0.916 4 | 0.000 9 | 0.000 11 | 0.000 1 | 0.000 9 | 0.000 1 | 0.102 12 | 0.499 9 | 0.296 14 | 0.463 6 | 0.089 5 | 0.304 1 | 0.740 3 | 0.401 16 | 0.010 7 | 0.000 1 | 0.560 4 | 0.000 2 | 0.000 2 | 0.709 2 | 0.652 10 | 0.000 3 | 0.000 1 | 0.000 1 | 0.143 8 | 0.000 7 | 0.000 3 | 0.609 5 | 0.000 1 | |||||||||||||||||||||||||||||
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
L3DETR-ScanNet_200 | 0.336 8 | 0.533 11 | 0.279 6 | 0.155 10 | 0.801 9 | 0.689 4 | 0.946 6 | 0.539 11 | 0.660 7 | 0.759 4 | 0.380 14 | 0.333 14 | 0.583 4 | 0.000 14 | 0.788 10 | 0.529 10 | 0.740 8 | 0.261 12 | 0.679 9 | 0.940 12 | 0.525 10 | 0.860 8 | 0.883 7 | 0.226 13 | 0.613 9 | 0.397 6 | 0.720 11 | 0.512 5 | 0.565 12 | 0.620 3 | 0.417 4 | 0.775 13 | 0.629 6 | 0.158 2 | 0.298 12 | 0.579 11 | 0.000 1 | 0.835 6 | 0.883 6 | 0.927 1 | 0.114 11 | 0.079 4 | 0.511 10 | 0.073 11 | 0.508 5 | 0.312 6 | 0.629 6 | 0.861 5 | 0.192 14 | 0.098 13 | 0.908 3 | 0.636 11 | 0.032 17 | 0.563 17 | 0.514 15 | 0.664 6 | 0.505 10 | 0.697 7 | 0.225 9 | 0.000 3 | 0.264 2 | 0.411 12 | 0.860 8 | 0.321 13 | 0.960 3 | 0.058 6 | 0.109 13 | 0.776 3 | 0.526 5 | 0.557 3 | 0.303 9 | 0.000 1 | 0.339 12 | 0.712 7 | 0.000 4 | 0.014 7 | 0.000 1 | 0.000 6 | 0.638 12 | 0.856 4 | 0.641 7 | 0.579 11 | 0.107 17 | 0.119 11 | 0.661 11 | 0.416 7 | 0.000 3 | 0.000 7 | 0.000 1 | 0.007 17 | 0.000 4 | 0.067 10 | 0.910 5 | 0.000 1 | 0.000 10 | 0.000 10 | 0.463 11 | 0.448 8 | 0.294 14 | 0.324 1 | 0.293 3 | 0.211 8 | 0.108 8 | 0.448 8 | 0.068 17 | 0.141 6 | 0.000 4 | 0.330 3 | 0.699 1 | 0.000 3 | 0.256 11 | 0.192 6 | 0.000 15 | 0.355 8 | 0.418 7 | 0.209 17 | 0.146 12 | 0.679 3 | 0.101 17 | 0.000 1 | 0.503 13 | 0.687 2 | 0.671 8 | 0.000 1 | 0.000 11 | 0.174 11 | 0.117 6 | 0.000 7 | 0.122 7 | 0.515 2 | 0.104 6 | 0.259 2 | 0.312 3 | 0.000 1 | 0.000 5 | 0.765 12 | 0.000 1 | 0.369 12 | 0.000 1 | 0.183 8 | 0.422 11 | 0.000 1 | 0.646 4 | 0.000 1 | 0.000 1 | 0.565 2 | 0.001 14 | 0.125 17 | 0.010 7 | 0.002 10 | 0.000 1 | 0.487 1 | 0.000 1 | 0.075 14 | 0.548 4 | 0.420 9 | 0.233 14 | 0.082 8 | 0.138 11 | 0.430 12 | 0.427 13 | 0.000 12 | 0.000 1 | 0.549 6 | 0.000 2 | 0.000 2 | 0.074 8 | 0.409 16 | 0.000 3 | 0.000 1 | 0.000 1 | 0.152 7 | 0.051 3 | 0.000 3 | 0.598 6 | 0.000 1 | |||||||||||||||||||||||||||||
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PTv3 ScanNet200 | 0.393 3 | 0.592 3 | 0.330 2 | 0.216 3 | 0.851 2 | 0.687 6 | 0.971 2 | 0.586 2 | 0.755 1 | 0.752 7 | 0.505 2 | 0.404 7 | 0.575 5 | 0.000 14 | 0.848 2 | 0.616 4 | 0.761 3 | 0.349 1 | 0.738 3 | 0.978 3 | 0.546 6 | 0.860 8 | 0.926 3 | 0.346 4 | 0.654 3 | 0.384 7 | 0.828 1 | 0.523 4 | 0.699 3 | 0.583 6 | 0.387 7 | 0.822 3 | 0.688 2 | 0.118 4 | 0.474 3 | 0.603 5 | 0.000 1 | 0.832 8 | 0.903 2 | 0.753 9 | 0.140 10 | 0.000 7 | 0.650 3 | 0.109 5 | 0.520 3 | 0.457 2 | 0.497 10 | 0.871 4 | 0.281 4 | 0.192 5 | 0.887 4 | 0.748 3 | 0.168 2 | 0.727 7 | 0.733 2 | 0.740 1 | 0.644 2 | 0.714 5 | 0.190 13 | 0.000 3 | 0.256 3 | 0.449 10 | 0.914 1 | 0.514 2 | 0.759 15 | 0.337 1 | 0.172 6 | 0.692 7 | 0.617 3 | 0.636 1 | 0.325 7 | 0.000 1 | 0.641 2 | 0.782 2 | 0.000 4 | 0.065 3 | 0.000 1 | 0.000 6 | 0.842 4 | 0.903 2 | 0.661 4 | 0.662 3 | 0.612 1 | 0.405 2 | 0.731 4 | 0.566 4 | 0.000 3 | 0.000 7 | 0.000 1 | 0.017 15 | 0.301 1 | 0.088 7 | 0.941 3 | 0.000 1 | 0.077 4 | 0.000 10 | 0.717 8 | 0.790 2 | 0.310 12 | 0.026 17 | 0.264 4 | 0.349 1 | 0.220 5 | 0.397 12 | 0.366 2 | 0.115 13 | 0.000 4 | 0.337 1 | 0.463 6 | 0.000 3 | 0.531 5 | 0.218 4 | 0.593 2 | 0.455 2 | 0.469 2 | 0.708 3 | 0.210 4 | 0.592 4 | 0.108 16 | 0.000 1 | 0.728 1 | 0.682 3 | 0.671 8 | 0.000 1 | 0.000 11 | 0.407 1 | 0.136 4 | 0.022 3 | 0.575 1 | 0.436 4 | 0.259 3 | 0.428 1 | 0.048 6 | 0.000 1 | 0.000 5 | 0.879 5 | 0.000 1 | 0.480 2 | 0.000 1 | 0.133 9 | 0.597 2 | 0.000 1 | 0.690 2 | 0.000 1 | 0.000 1 | 0.009 16 | 0.000 15 | 0.921 3 | 0.000 9 | 0.151 5 | 0.000 1 | 0.000 9 | 0.000 1 | 0.109 8 | 0.494 11 | 0.622 2 | 0.394 9 | 0.073 12 | 0.141 7 | 0.798 2 | 0.528 8 | 0.026 5 | 0.000 1 | 0.551 5 | 0.000 2 | 0.000 2 | 0.134 7 | 0.717 8 | 0.000 3 | 0.000 1 | 0.000 1 | 0.188 4 | 0.000 7 | 0.000 3 | 0.791 3 | 0.000 1 | |||||||||||||||||||||||||||||
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
OctFormer ScanNet200 | ![]() | 0.326 13 | 0.539 10 | 0.265 10 | 0.131 12 | 0.806 8 | 0.670 12 | 0.943 9 | 0.535 12 | 0.662 4 | 0.705 16 | 0.423 9 | 0.407 6 | 0.505 13 | 0.003 11 | 0.765 13 | 0.582 7 | 0.686 15 | 0.227 16 | 0.680 8 | 0.943 10 | 0.601 2 | 0.854 10 | 0.892 6 | 0.335 6 | 0.417 17 | 0.357 10 | 0.724 10 | 0.453 11 | 0.632 7 | 0.596 5 | 0.432 3 | 0.783 11 | 0.512 16 | 0.021 12 | 0.244 15 | 0.637 2 | 0.000 1 | 0.787 12 | 0.873 9 | 0.743 11 | 0.000 17 | 0.000 7 | 0.534 9 | 0.110 4 | 0.499 6 | 0.289 10 | 0.626 7 | 0.620 12 | 0.168 15 | 0.204 4 | 0.849 10 | 0.679 8 | 0.117 5 | 0.633 11 | 0.684 3 | 0.650 8 | 0.552 5 | 0.684 9 | 0.312 3 | 0.000 3 | 0.175 11 | 0.429 11 | 0.865 5 | 0.413 4 | 0.837 12 | 0.000 7 | 0.145 8 | 0.626 9 | 0.451 8 | 0.487 11 | 0.513 3 | 0.000 1 | 0.529 7 | 0.613 12 | 0.000 4 | 0.033 6 | 0.000 1 | 0.000 6 | 0.828 5 | 0.871 3 | 0.622 9 | 0.587 9 | 0.411 7 | 0.137 10 | 0.645 14 | 0.343 12 | 0.000 3 | 0.000 7 | 0.000 1 | 0.022 13 | 0.000 4 | 0.026 17 | 0.829 11 | 0.000 1 | 0.022 8 | 0.089 6 | 0.842 4 | 0.253 14 | 0.318 11 | 0.296 2 | 0.178 11 | 0.291 3 | 0.224 3 | 0.584 2 | 0.200 14 | 0.132 8 | 0.000 4 | 0.128 11 | 0.227 13 | 0.000 3 | 0.230 13 | 0.047 11 | 0.149 8 | 0.331 10 | 0.412 9 | 0.618 7 | 0.164 10 | 0.102 11 | 0.522 3 | 0.000 1 | 0.655 4 | 0.378 12 | 0.469 15 | 0.000 1 | 0.000 11 | 0.000 12 | 0.105 9 | 0.000 7 | 0.000 10 | 0.483 3 | 0.000 12 | 0.000 8 | 0.028 8 | 0.000 1 | 0.000 5 | 0.906 1 | 0.000 1 | 0.339 15 | 0.000 1 | 0.000 12 | 0.457 9 | 0.000 1 | 0.612 8 | 0.000 1 | 0.000 1 | 0.408 4 | 0.000 15 | 0.900 10 | 0.000 9 | 0.000 11 | 0.000 1 | 0.029 8 | 0.000 1 | 0.074 15 | 0.455 15 | 0.479 6 | 0.427 7 | 0.079 9 | 0.140 8 | 0.496 8 | 0.414 14 | 0.022 6 | 0.000 1 | 0.471 13 | 0.000 2 | 0.000 2 | 0.000 11 | 0.722 7 | 0.000 3 | 0.000 1 | 0.000 1 | 0.138 13 | 0.000 7 | 0.000 3 | 0.000 11 | 0.000 1 | ||||||||||||||||||||||||||||
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
BFANet ScanNet200 | ![]() | 0.360 5 | 0.553 7 | 0.293 5 | 0.193 5 | 0.827 4 | 0.689 4 | 0.970 3 | 0.528 13 | 0.661 6 | 0.753 6 | 0.436 8 | 0.378 8 | 0.469 15 | 0.042 7 | 0.810 3 | 0.654 2 | 0.760 4 | 0.266 10 | 0.659 10 | 0.973 4 | 0.574 3 | 0.849 11 | 0.897 5 | 0.382 3 | 0.546 13 | 0.372 9 | 0.698 14 | 0.491 9 | 0.617 10 | 0.526 10 | 0.436 1 | 0.764 14 | 0.476 17 | 0.101 5 | 0.409 6 | 0.585 10 | 0.000 1 | 0.835 6 | 0.901 3 | 0.810 5 | 0.102 14 | 0.000 7 | 0.688 2 | 0.096 6 | 0.483 10 | 0.264 12 | 0.612 9 | 0.591 16 | 0.358 2 | 0.161 6 | 0.863 5 | 0.707 4 | 0.128 4 | 0.814 2 | 0.669 4 | 0.629 10 | 0.563 4 | 0.651 14 | 0.258 5 | 0.000 3 | 0.194 10 | 0.494 9 | 0.806 12 | 0.394 6 | 0.953 5 | 0.000 7 | 0.233 1 | 0.757 4 | 0.508 6 | 0.556 4 | 0.476 4 | 0.000 1 | 0.573 5 | 0.741 6 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.000 17 | 0.852 5 | 0.678 3 | 0.616 6 | 0.460 5 | 0.338 3 | 0.710 5 | 0.534 5 | 0.000 3 | 0.025 4 | 0.000 1 | 0.043 3 | 0.000 4 | 0.056 12 | 0.493 17 | 0.000 1 | 0.000 10 | 0.109 5 | 0.785 7 | 0.590 6 | 0.298 13 | 0.282 3 | 0.143 13 | 0.262 4 | 0.053 11 | 0.526 4 | 0.337 5 | 0.215 1 | 0.000 4 | 0.135 9 | 0.510 4 | 0.000 3 | 0.596 4 | 0.043 14 | 0.511 3 | 0.321 12 | 0.459 3 | 0.772 2 | 0.124 13 | 0.060 14 | 0.266 6 | 0.000 1 | 0.574 9 | 0.568 9 | 0.653 10 | 0.000 1 | 0.093 1 | 0.298 4 | 0.239 3 | 0.000 7 | 0.516 2 | 0.129 14 | 0.284 2 | 0.000 8 | 0.431 1 | 0.000 1 | 0.000 5 | 0.848 6 | 0.000 1 | 0.492 1 | 0.000 1 | 0.376 3 | 0.522 6 | 0.000 1 | 0.469 17 | 0.000 1 | 0.000 1 | 0.330 6 | 0.151 10 | 0.875 14 | 0.000 9 | 0.254 4 | 0.000 1 | 0.000 9 | 0.000 1 | 0.088 13 | 0.661 1 | 0.481 5 | 0.255 12 | 0.105 1 | 0.139 9 | 0.666 5 | 0.641 5 | 0.000 12 | 0.000 1 | 0.614 2 | 0.000 2 | 0.000 2 | 0.000 11 | 0.921 2 | 0.000 3 | 0.000 1 | 0.000 1 | 0.497 1 | 0.000 7 | 0.000 3 | 0.000 11 | 0.000 1 | ||||||||||||||||||||||||||||
Weiguang Zhao, Rui Zhang, Qiufeng Wang, Guangliang Cheng, Kaizhu Huang: BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis. CVPR 2025 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
GSTran | 0.334 10 | 0.533 12 | 0.250 12 | 0.179 8 | 0.799 11 | 0.684 7 | 0.940 10 | 0.554 9 | 0.633 11 | 0.741 10 | 0.405 11 | 0.337 12 | 0.560 9 | 0.060 5 | 0.794 8 | 0.517 13 | 0.732 11 | 0.274 5 | 0.647 12 | 0.948 7 | 0.459 16 | 0.849 11 | 0.864 9 | 0.306 9 | 0.648 5 | 0.282 14 | 0.717 12 | 0.496 7 | 0.624 9 | 0.533 8 | 0.363 9 | 0.821 4 | 0.573 14 | 0.009 15 | 0.411 4 | 0.593 9 | 0.000 1 | 0.841 5 | 0.873 9 | 0.704 14 | 0.242 5 | 0.000 7 | 0.495 11 | 0.041 16 | 0.487 8 | 0.304 8 | 0.439 13 | 0.613 13 | 0.133 17 | 0.055 16 | 0.853 8 | 0.634 12 | 0.075 12 | 0.791 5 | 0.601 9 | 0.574 16 | 0.483 13 | 0.669 11 | 0.217 10 | 0.000 3 | 0.198 8 | 0.518 6 | 0.782 14 | 0.345 11 | 0.914 6 | 0.273 5 | 0.193 3 | 0.598 14 | 0.440 9 | 0.499 8 | 0.570 1 | 0.000 1 | 0.381 11 | 0.775 4 | 0.000 4 | 0.063 5 | 0.000 1 | 0.000 6 | 0.712 8 | 0.752 13 | 0.507 12 | 0.512 16 | 0.158 16 | 0.036 13 | 0.773 2 | 0.361 11 | 0.000 3 | 0.000 7 | 0.000 1 | 0.032 7 | 0.000 4 | 0.032 15 | 0.651 15 | 0.000 1 | 0.000 10 | 0.000 10 | 0.831 5 | 0.595 4 | 0.273 16 | 0.229 7 | 0.200 9 | 0.191 10 | 0.000 14 | 0.425 9 | 0.233 12 | 0.125 11 | 0.000 4 | 0.279 5 | 0.213 15 | 0.003 1 | 0.608 3 | 0.044 12 | 0.138 9 | 0.321 12 | 0.408 11 | 0.593 10 | 0.198 5 | 0.205 8 | 0.139 13 | 0.000 1 | 0.614 7 | 0.609 7 | 0.838 4 | 0.000 1 | 0.014 6 | 0.260 6 | 0.080 12 | 0.010 5 | 0.000 10 | 0.136 13 | 0.136 4 | 0.047 6 | 0.000 11 | 0.000 1 | 0.787 3 | 0.797 10 | 0.000 1 | 0.354 14 | 0.000 1 | 0.372 4 | 0.357 14 | 0.000 1 | 0.507 16 | 0.000 1 | 0.000 1 | 0.121 11 | 0.423 3 | 0.903 8 | 0.028 4 | 0.089 7 | 0.000 1 | 0.252 4 | 0.000 1 | 0.072 17 | 0.465 12 | 0.340 12 | 0.189 16 | 0.020 16 | 0.011 16 | 0.320 16 | 0.606 7 | 0.060 3 | 0.000 1 | 0.496 9 | 0.000 2 | 0.000 2 | 0.070 9 | 0.618 13 | 0.000 3 | 0.000 1 | 0.000 1 | 0.139 11 | 0.047 4 | 0.000 3 | 0.558 8 | 0.000 1 | |||||||||||||||||||||||||||||
IMFSegNet | 0.334 9 | 0.532 13 | 0.251 11 | 0.179 7 | 0.799 11 | 0.683 8 | 0.940 10 | 0.555 8 | 0.631 12 | 0.740 11 | 0.406 10 | 0.336 13 | 0.560 9 | 0.062 4 | 0.795 7 | 0.518 12 | 0.733 10 | 0.274 5 | 0.646 13 | 0.947 8 | 0.458 17 | 0.848 13 | 0.862 10 | 0.305 10 | 0.649 4 | 0.284 13 | 0.713 13 | 0.495 8 | 0.626 8 | 0.527 9 | 0.363 9 | 0.820 5 | 0.574 13 | 0.010 14 | 0.411 4 | 0.597 7 | 0.000 1 | 0.842 4 | 0.873 9 | 0.704 14 | 0.246 4 | 0.000 7 | 0.495 11 | 0.041 16 | 0.486 9 | 0.305 7 | 0.444 12 | 0.604 15 | 0.134 16 | 0.055 16 | 0.852 9 | 0.633 13 | 0.076 9 | 0.792 4 | 0.612 8 | 0.573 17 | 0.484 12 | 0.668 12 | 0.216 12 | 0.000 3 | 0.197 9 | 0.518 6 | 0.784 13 | 0.344 12 | 0.908 7 | 0.283 4 | 0.190 4 | 0.599 13 | 0.439 10 | 0.496 10 | 0.569 2 | 0.000 1 | 0.392 9 | 0.776 3 | 0.000 4 | 0.064 4 | 0.000 1 | 0.000 6 | 0.710 9 | 0.756 12 | 0.508 11 | 0.512 16 | 0.159 15 | 0.034 14 | 0.773 2 | 0.363 10 | 0.000 3 | 0.000 7 | 0.000 1 | 0.032 7 | 0.000 4 | 0.029 16 | 0.648 16 | 0.000 1 | 0.000 10 | 0.000 10 | 0.830 6 | 0.595 4 | 0.274 15 | 0.228 8 | 0.206 8 | 0.188 12 | 0.000 14 | 0.425 9 | 0.237 11 | 0.123 12 | 0.000 4 | 0.277 6 | 0.214 14 | 0.003 1 | 0.610 2 | 0.044 12 | 0.124 10 | 0.320 14 | 0.408 11 | 0.594 9 | 0.196 7 | 0.213 7 | 0.139 13 | 0.000 1 | 0.615 6 | 0.618 6 | 0.839 3 | 0.000 1 | 0.014 6 | 0.260 6 | 0.080 12 | 0.025 2 | 0.000 10 | 0.139 12 | 0.135 5 | 0.035 7 | 0.000 11 | 0.000 1 | 0.793 2 | 0.799 9 | 0.000 1 | 0.357 13 | 0.000 1 | 0.369 5 | 0.359 13 | 0.000 1 | 0.512 15 | 0.000 1 | 0.000 1 | 0.120 12 | 0.424 2 | 0.903 8 | 0.027 5 | 0.091 6 | 0.000 1 | 0.245 5 | 0.000 1 | 0.073 16 | 0.457 14 | 0.340 12 | 0.191 15 | 0.021 15 | 0.009 17 | 0.322 15 | 0.608 6 | 0.060 3 | 0.000 1 | 0.494 10 | 0.000 2 | 0.000 2 | 0.068 10 | 0.624 11 | 0.000 3 | 0.000 1 | 0.000 1 | 0.139 11 | 0.047 4 | 0.000 3 | 0.561 7 | 0.000 1 | |||||||||||||||||||||||||||||
ODIN - Sem200 | ![]() | 0.368 4 | 0.562 4 | 0.297 4 | 0.207 4 | 0.800 10 | 0.669 13 | 0.940 10 | 0.575 3 | 0.654 9 | 0.749 8 | 0.487 3 | 0.589 1 | 0.609 2 | 0.001 12 | 0.769 12 | 0.561 8 | 0.752 6 | 0.274 5 | 0.682 6 | 0.926 13 | 0.554 4 | 0.833 14 | 0.921 4 | 0.389 2 | 0.599 10 | 0.591 1 | 0.787 8 | 0.550 2 | 0.657 5 | 0.610 4 | 0.334 13 | 0.803 8 | 0.661 4 | 0.090 6 | 0.408 7 | 0.373 15 | 0.000 1 | 0.912 2 | 0.796 17 | 0.501 17 | 0.169 8 | 0.000 7 | 0.641 4 | 0.196 1 | 0.380 17 | 0.397 3 | 0.641 5 | 0.740 9 | 0.862 1 | 0.213 3 | 0.857 6 | 0.685 7 | 0.216 1 | 0.578 16 | 0.557 10 | 0.685 5 | 0.523 8 | 0.581 16 | 0.312 3 | 0.000 3 | 0.065 15 | 0.000 17 | 0.871 3 | 0.359 8 | 0.988 2 | 0.321 2 | 0.090 16 | 0.704 6 | 0.631 2 | 0.393 15 | 0.246 11 | 0.000 1 | 0.482 8 | 0.565 15 | 0.000 4 | 0.000 9 | 0.000 1 | 0.181 1 | 0.913 1 | 0.468 16 | 0.632 8 | 0.642 5 | 0.259 11 | 0.000 17 | 0.832 1 | 0.663 1 | 0.000 3 | 0.081 1 | 0.000 1 | 0.048 2 | 0.000 4 | 0.376 1 | 0.898 7 | 0.000 1 | 0.157 1 | 0.000 10 | 0.870 3 | 0.000 17 | 0.400 5 | 0.265 4 | 0.242 5 | 0.227 6 | 0.539 1 | 0.370 14 | 0.214 13 | 0.129 10 | 0.000 4 | 0.131 10 | 0.054 17 | 0.000 3 | 0.358 9 | 0.491 1 | 0.462 4 | 0.434 3 | 0.346 15 | 0.454 15 | 0.316 2 | 0.814 1 | 0.828 2 | 0.000 1 | 0.000 17 | 0.220 17 | 0.612 11 | 0.000 1 | 0.000 11 | 0.373 2 | 0.378 2 | 0.000 7 | 0.429 4 | 0.152 11 | 0.077 9 | 0.166 4 | 0.202 5 | 0.000 1 | 0.000 5 | 0.441 14 | 0.000 1 | 0.440 6 | 0.000 1 | 0.000 12 | 0.655 1 | 0.000 1 | 0.626 7 | 0.000 1 | 0.000 1 | 0.228 9 | 0.487 1 | 0.784 16 | 0.000 9 | 0.301 3 | 0.000 1 | 0.426 2 | 0.000 1 | 0.108 9 | 0.460 13 | 0.590 4 | 0.775 1 | 0.088 6 | 0.119 15 | 0.485 9 | 0.791 1 | 0.000 12 | 0.000 1 | 0.256 17 | 0.000 2 | 0.000 2 | 0.000 11 | 0.885 3 | 0.303 1 | 0.000 1 | 0.000 1 | 0.127 16 | 0.000 7 | 0.000 3 | 0.894 2 | 0.000 1 | ||||||||||||||||||||||||||||
Ayush Jain, Pushkal Katara, Nikolaos Gkanatsios, Adam W. Harley, Gabriel Sarch, Kriti Aggarwal, Vishrav Chaudhary, Katerina Fragkiadaki: ODIN: A Single Model for 2D and 3D Segmentation. CVPR 2024 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
LGround | ![]() | 0.272 15 | 0.485 15 | 0.184 15 | 0.106 15 | 0.778 15 | 0.676 11 | 0.932 15 | 0.479 17 | 0.572 15 | 0.718 14 | 0.399 12 | 0.265 15 | 0.453 16 | 0.085 3 | 0.745 15 | 0.446 15 | 0.726 13 | 0.232 15 | 0.622 15 | 0.901 15 | 0.512 11 | 0.826 15 | 0.786 16 | 0.178 16 | 0.549 12 | 0.277 15 | 0.659 15 | 0.381 15 | 0.518 14 | 0.295 17 | 0.323 14 | 0.777 12 | 0.599 10 | 0.028 10 | 0.321 11 | 0.363 16 | 0.000 1 | 0.708 15 | 0.858 14 | 0.746 10 | 0.063 15 | 0.022 5 | 0.457 15 | 0.077 10 | 0.476 11 | 0.243 15 | 0.402 14 | 0.397 17 | 0.233 10 | 0.077 15 | 0.720 17 | 0.610 16 | 0.103 6 | 0.629 12 | 0.437 17 | 0.626 11 | 0.446 14 | 0.702 6 | 0.190 13 | 0.005 1 | 0.058 16 | 0.322 14 | 0.702 16 | 0.244 15 | 0.768 14 | 0.000 7 | 0.134 12 | 0.552 15 | 0.279 16 | 0.395 14 | 0.147 16 | 0.000 1 | 0.207 15 | 0.612 13 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.658 11 | 0.566 14 | 0.323 15 | 0.525 15 | 0.229 12 | 0.179 8 | 0.467 17 | 0.154 16 | 0.000 3 | 0.002 5 | 0.000 1 | 0.051 1 | 0.000 4 | 0.127 4 | 0.703 12 | 0.000 1 | 0.000 10 | 0.216 1 | 0.112 16 | 0.358 11 | 0.547 2 | 0.187 9 | 0.092 16 | 0.156 17 | 0.055 10 | 0.296 15 | 0.252 9 | 0.143 5 | 0.000 4 | 0.014 14 | 0.398 7 | 0.000 3 | 0.028 16 | 0.173 7 | 0.000 15 | 0.265 16 | 0.348 14 | 0.415 16 | 0.179 8 | 0.019 16 | 0.218 8 | 0.000 1 | 0.597 8 | 0.274 16 | 0.565 13 | 0.000 1 | 0.012 8 | 0.000 12 | 0.039 16 | 0.022 3 | 0.000 10 | 0.117 15 | 0.000 12 | 0.000 8 | 0.000 11 | 0.000 1 | 0.000 5 | 0.324 16 | 0.000 1 | 0.384 9 | 0.000 1 | 0.000 12 | 0.251 17 | 0.000 1 | 0.566 11 | 0.000 1 | 0.000 1 | 0.066 14 | 0.404 4 | 0.886 13 | 0.199 2 | 0.000 11 | 0.000 1 | 0.059 7 | 0.000 1 | 0.136 1 | 0.540 5 | 0.127 17 | 0.295 11 | 0.085 7 | 0.143 6 | 0.514 7 | 0.413 15 | 0.000 12 | 0.000 1 | 0.498 8 | 0.000 2 | 0.000 2 | 0.000 11 | 0.623 12 | 0.000 3 | 0.000 1 | 0.000 1 | 0.132 15 | 0.000 7 | 0.000 3 | 0.000 11 | 0.000 1 | ||||||||||||||||||||||||||||
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CSC-Pretrain | ![]() | 0.249 17 | 0.455 17 | 0.171 16 | 0.079 17 | 0.766 17 | 0.659 15 | 0.930 17 | 0.494 14 | 0.542 17 | 0.700 17 | 0.314 17 | 0.215 17 | 0.430 17 | 0.121 1 | 0.697 17 | 0.441 16 | 0.683 16 | 0.235 14 | 0.609 17 | 0.895 16 | 0.476 15 | 0.816 16 | 0.770 17 | 0.186 14 | 0.634 6 | 0.216 17 | 0.734 9 | 0.340 16 | 0.471 16 | 0.307 16 | 0.293 17 | 0.591 17 | 0.542 15 | 0.076 7 | 0.205 16 | 0.464 14 | 0.000 1 | 0.484 17 | 0.832 16 | 0.766 7 | 0.052 16 | 0.000 7 | 0.413 16 | 0.059 14 | 0.418 15 | 0.222 16 | 0.318 17 | 0.609 14 | 0.206 13 | 0.112 9 | 0.743 14 | 0.625 14 | 0.076 9 | 0.579 15 | 0.548 13 | 0.590 14 | 0.371 16 | 0.552 17 | 0.081 16 | 0.003 2 | 0.142 13 | 0.201 16 | 0.638 17 | 0.233 16 | 0.686 17 | 0.000 7 | 0.142 9 | 0.444 17 | 0.375 13 | 0.247 17 | 0.198 14 | 0.000 1 | 0.128 17 | 0.454 17 | 0.019 2 | 0.097 1 | 0.000 1 | 0.000 6 | 0.553 14 | 0.557 15 | 0.373 13 | 0.545 14 | 0.164 14 | 0.014 16 | 0.547 16 | 0.174 15 | 0.000 3 | 0.002 5 | 0.000 1 | 0.037 4 | 0.000 4 | 0.063 11 | 0.664 14 | 0.000 1 | 0.000 10 | 0.130 2 | 0.170 14 | 0.152 16 | 0.335 10 | 0.079 14 | 0.110 15 | 0.175 14 | 0.098 9 | 0.175 17 | 0.166 15 | 0.045 17 | 0.207 2 | 0.014 14 | 0.465 5 | 0.000 3 | 0.001 17 | 0.001 17 | 0.046 12 | 0.299 15 | 0.327 16 | 0.537 12 | 0.033 16 | 0.012 17 | 0.186 10 | 0.000 1 | 0.205 15 | 0.377 13 | 0.463 16 | 0.000 1 | 0.058 3 | 0.000 12 | 0.055 15 | 0.041 1 | 0.000 10 | 0.105 16 | 0.000 12 | 0.000 8 | 0.000 11 | 0.000 1 | 0.000 5 | 0.398 15 | 0.000 1 | 0.308 17 | 0.000 1 | 0.000 12 | 0.319 15 | 0.000 1 | 0.543 12 | 0.000 1 | 0.000 1 | 0.062 15 | 0.004 13 | 0.862 15 | 0.000 9 | 0.000 11 | 0.000 1 | 0.000 9 | 0.000 1 | 0.123 5 | 0.316 16 | 0.225 15 | 0.250 13 | 0.094 3 | 0.180 5 | 0.332 14 | 0.441 11 | 0.000 12 | 0.000 1 | 0.310 16 | 0.000 2 | 0.000 2 | 0.000 11 | 0.592 14 | 0.000 3 | 0.000 1 | 0.000 1 | 0.203 3 | 0.000 7 | 0.000 3 | 0.000 11 | 0.000 1 | ||||||||||||||||||||||||||||
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Minkowski 34D | ![]() | 0.253 16 | 0.463 16 | 0.154 17 | 0.102 16 | 0.771 16 | 0.650 16 | 0.932 15 | 0.483 16 | 0.571 16 | 0.710 15 | 0.331 16 | 0.250 16 | 0.492 14 | 0.044 6 | 0.703 16 | 0.419 17 | 0.606 17 | 0.227 16 | 0.621 16 | 0.865 17 | 0.531 8 | 0.771 17 | 0.813 14 | 0.291 11 | 0.484 15 | 0.242 16 | 0.612 17 | 0.282 17 | 0.440 17 | 0.351 15 | 0.299 15 | 0.622 16 | 0.593 11 | 0.027 11 | 0.293 13 | 0.310 17 | 0.000 1 | 0.757 14 | 0.858 14 | 0.737 12 | 0.150 9 | 0.164 1 | 0.368 17 | 0.084 8 | 0.381 16 | 0.142 17 | 0.357 15 | 0.720 10 | 0.214 12 | 0.092 14 | 0.724 16 | 0.596 17 | 0.056 14 | 0.655 9 | 0.525 14 | 0.581 15 | 0.352 17 | 0.594 15 | 0.056 17 | 0.000 3 | 0.014 17 | 0.224 15 | 0.772 15 | 0.205 17 | 0.720 16 | 0.000 7 | 0.159 7 | 0.531 16 | 0.163 17 | 0.294 16 | 0.136 17 | 0.000 1 | 0.169 16 | 0.589 14 | 0.000 4 | 0.000 9 | 0.000 1 | 0.002 4 | 0.663 10 | 0.466 17 | 0.265 17 | 0.582 10 | 0.337 10 | 0.016 15 | 0.559 15 | 0.084 17 | 0.000 3 | 0.000 7 | 0.000 1 | 0.036 5 | 0.000 4 | 0.125 5 | 0.670 13 | 0.000 1 | 0.102 3 | 0.071 8 | 0.164 15 | 0.406 9 | 0.386 7 | 0.046 16 | 0.068 17 | 0.159 15 | 0.117 6 | 0.284 16 | 0.111 16 | 0.094 16 | 0.000 4 | 0.000 17 | 0.197 16 | 0.000 3 | 0.044 15 | 0.013 15 | 0.002 14 | 0.228 17 | 0.307 17 | 0.588 11 | 0.025 17 | 0.545 5 | 0.134 15 | 0.000 1 | 0.655 4 | 0.302 14 | 0.282 17 | 0.000 1 | 0.060 2 | 0.000 12 | 0.035 17 | 0.000 7 | 0.000 10 | 0.097 17 | 0.000 12 | 0.000 8 | 0.005 10 | 0.000 1 | 0.000 5 | 0.096 17 | 0.000 1 | 0.334 16 | 0.000 1 | 0.000 12 | 0.274 16 | 0.000 1 | 0.513 14 | 0.000 1 | 0.000 1 | 0.280 8 | 0.194 9 | 0.897 11 | 0.000 9 | 0.000 11 | 0.000 1 | 0.000 9 | 0.000 1 | 0.108 9 | 0.279 17 | 0.189 16 | 0.141 17 | 0.059 14 | 0.272 2 | 0.307 17 | 0.445 10 | 0.003 10 | 0.000 1 | 0.353 15 | 0.000 2 | 0.026 1 | 0.000 11 | 0.581 15 | 0.001 2 | 0.000 1 | 0.000 1 | 0.093 17 | 0.002 6 | 0.000 3 | 0.000 11 | 0.000 1 | ||||||||||||||||||||||||||||
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 |