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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OA-CNN-L_ScanNet200 | 0.333 5 | 0.558 2 | 0.269 5 | 0.124 7 | 0.821 2 | 0.703 1 | 0.946 3 | 0.569 2 | 0.662 2 | 0.748 5 | 0.487 2 | 0.455 1 | 0.572 4 | 0.000 9 | 0.789 4 | 0.534 5 | 0.736 5 | 0.271 3 | 0.713 2 | 0.949 3 | 0.498 10 | 0.877 2 | 0.860 5 | 0.332 3 | 0.706 1 | 0.474 1 | 0.788 5 | 0.406 7 | 0.637 3 | 0.495 5 | 0.355 6 | 0.805 3 | 0.592 9 | 0.015 10 | 0.396 2 | 0.602 5 | 0.000 1 | 0.799 5 | 0.876 4 | 0.713 11 | 0.276 1 | 0.000 7 | 0.493 7 | 0.080 5 | 0.448 9 | 0.363 2 | 0.661 2 | 0.833 5 | 0.262 3 | 0.125 3 | 0.823 6 | 0.665 5 | 0.076 6 | 0.720 3 | 0.557 5 | 0.637 6 | 0.517 5 | 0.672 8 | 0.227 5 | 0.000 3 | 0.158 7 | 0.496 4 | 0.843 8 | 0.352 6 | 0.835 7 | 0.000 3 | 0.103 9 | 0.711 2 | 0.527 2 | 0.526 4 | 0.320 4 | 0.000 1 | 0.568 3 | 0.625 6 | 0.067 1 | 0.000 6 | 0.000 1 | 0.001 2 | 0.806 3 | 0.836 5 | 0.621 6 | 0.591 4 | 0.373 5 | 0.314 4 | 0.668 4 | 0.398 5 | 0.003 2 | 0.000 4 | 0.000 1 | 0.016 10 | 0.024 2 | 0.043 9 | 0.906 4 | 0.000 1 | 0.052 4 | 0.000 7 | 0.384 6 | 0.330 8 | 0.342 5 | 0.100 6 | 0.223 4 | 0.183 7 | 0.112 4 | 0.476 4 | 0.313 4 | 0.130 6 | 0.196 2 | 0.112 6 | 0.370 8 | 0.000 1 | 0.234 6 | 0.071 6 | 0.160 3 | 0.403 3 | 0.398 8 | 0.492 9 | 0.197 2 | 0.076 8 | 0.272 3 | 0.000 1 | 0.200 11 | 0.560 5 | 0.735 3 | 0.000 1 | 0.000 6 | 0.000 6 | 0.110 4 | 0.002 4 | 0.021 5 | 0.412 5 | 0.000 6 | 0.000 4 | 0.000 7 | 0.000 1 | 0.000 1 | 0.794 6 | 0.000 1 | 0.445 2 | 0.000 1 | 0.022 5 | 0.509 5 | 0.000 1 | 0.517 10 | 0.000 1 | 0.000 1 | 0.001 11 | 0.245 2 | 0.915 4 | 0.024 2 | 0.089 2 | 0.000 1 | 0.262 2 | 0.000 1 | 0.103 8 | 0.524 4 | 0.392 7 | 0.515 2 | 0.013 11 | 0.251 4 | 0.411 9 | 0.662 1 | 0.001 7 | 0.000 1 | 0.473 7 | 0.000 2 | 0.000 2 | 0.150 4 | 0.699 5 | 0.000 2 | 0.000 1 | 0.000 1 | 0.166 4 | 0.000 4 | 0.024 1 | 0.000 6 | 0.000 1 | |||||||||||||||||||||||||||||
L3DETR-ScanNet_200 | 0.336 4 | 0.533 7 | 0.279 2 | 0.155 4 | 0.801 6 | 0.689 2 | 0.946 3 | 0.539 6 | 0.660 4 | 0.759 2 | 0.380 8 | 0.333 8 | 0.583 1 | 0.000 9 | 0.788 5 | 0.529 6 | 0.740 4 | 0.261 6 | 0.679 6 | 0.940 7 | 0.525 7 | 0.860 6 | 0.883 3 | 0.226 7 | 0.613 6 | 0.397 3 | 0.720 8 | 0.512 4 | 0.565 6 | 0.620 1 | 0.417 3 | 0.775 8 | 0.629 3 | 0.158 2 | 0.298 6 | 0.579 7 | 0.000 1 | 0.835 1 | 0.883 3 | 0.927 1 | 0.114 7 | 0.079 4 | 0.511 6 | 0.073 7 | 0.508 3 | 0.312 3 | 0.629 3 | 0.861 4 | 0.192 10 | 0.098 9 | 0.908 1 | 0.636 7 | 0.032 11 | 0.563 11 | 0.514 9 | 0.664 3 | 0.505 6 | 0.697 5 | 0.225 6 | 0.000 3 | 0.264 1 | 0.411 7 | 0.860 6 | 0.321 7 | 0.960 1 | 0.058 2 | 0.109 8 | 0.776 1 | 0.526 3 | 0.557 2 | 0.303 5 | 0.000 1 | 0.339 6 | 0.712 3 | 0.000 4 | 0.014 4 | 0.000 1 | 0.000 3 | 0.638 7 | 0.856 3 | 0.641 4 | 0.579 7 | 0.107 11 | 0.119 9 | 0.661 5 | 0.416 3 | 0.000 3 | 0.000 4 | 0.000 1 | 0.007 11 | 0.000 3 | 0.067 7 | 0.910 3 | 0.000 1 | 0.000 7 | 0.000 7 | 0.463 5 | 0.448 4 | 0.294 10 | 0.324 1 | 0.293 1 | 0.211 4 | 0.108 5 | 0.448 5 | 0.068 11 | 0.141 3 | 0.000 3 | 0.330 2 | 0.699 1 | 0.000 1 | 0.256 5 | 0.192 3 | 0.000 9 | 0.355 5 | 0.418 4 | 0.209 11 | 0.146 7 | 0.679 1 | 0.101 11 | 0.000 1 | 0.503 9 | 0.687 1 | 0.671 4 | 0.000 1 | 0.000 6 | 0.174 5 | 0.117 2 | 0.000 5 | 0.122 4 | 0.515 2 | 0.104 2 | 0.259 2 | 0.312 1 | 0.000 1 | 0.000 1 | 0.765 7 | 0.000 1 | 0.369 8 | 0.000 1 | 0.183 3 | 0.422 8 | 0.000 1 | 0.646 2 | 0.000 1 | 0.000 1 | 0.565 1 | 0.001 8 | 0.125 11 | 0.010 3 | 0.002 4 | 0.000 1 | 0.487 1 | 0.000 1 | 0.075 10 | 0.548 2 | 0.420 5 | 0.233 10 | 0.082 6 | 0.138 9 | 0.430 8 | 0.427 7 | 0.000 8 | 0.000 1 | 0.549 3 | 0.000 2 | 0.000 2 | 0.074 6 | 0.409 10 | 0.000 2 | 0.000 1 | 0.000 1 | 0.152 5 | 0.051 2 | 0.000 2 | 0.598 3 | 0.000 1 | |||||||||||||||||||||||||||||
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PTv3 ScanNet200 | 0.393 1 | 0.592 1 | 0.330 1 | 0.216 1 | 0.851 1 | 0.687 3 | 0.971 1 | 0.586 1 | 0.755 1 | 0.752 4 | 0.505 1 | 0.404 4 | 0.575 2 | 0.000 9 | 0.848 1 | 0.616 1 | 0.761 1 | 0.349 1 | 0.738 1 | 0.978 1 | 0.546 3 | 0.860 6 | 0.926 1 | 0.346 1 | 0.654 3 | 0.384 4 | 0.828 1 | 0.523 3 | 0.699 1 | 0.583 3 | 0.387 5 | 0.822 1 | 0.688 1 | 0.118 4 | 0.474 1 | 0.603 4 | 0.000 1 | 0.832 2 | 0.903 1 | 0.753 7 | 0.140 6 | 0.000 7 | 0.650 1 | 0.109 2 | 0.520 1 | 0.457 1 | 0.497 6 | 0.871 3 | 0.281 1 | 0.192 2 | 0.887 2 | 0.748 1 | 0.168 1 | 0.727 2 | 0.733 1 | 0.740 1 | 0.644 1 | 0.714 3 | 0.190 7 | 0.000 3 | 0.256 2 | 0.449 5 | 0.914 1 | 0.514 1 | 0.759 9 | 0.337 1 | 0.172 3 | 0.692 3 | 0.617 1 | 0.636 1 | 0.325 3 | 0.000 1 | 0.641 1 | 0.782 1 | 0.000 4 | 0.065 2 | 0.000 1 | 0.000 3 | 0.842 1 | 0.903 1 | 0.661 1 | 0.662 2 | 0.612 1 | 0.405 2 | 0.731 1 | 0.566 1 | 0.000 3 | 0.000 4 | 0.000 1 | 0.017 9 | 0.301 1 | 0.088 4 | 0.941 1 | 0.000 1 | 0.077 2 | 0.000 7 | 0.717 2 | 0.790 1 | 0.310 9 | 0.026 11 | 0.264 2 | 0.349 1 | 0.220 2 | 0.397 7 | 0.366 1 | 0.115 7 | 0.000 3 | 0.337 1 | 0.463 4 | 0.000 1 | 0.531 1 | 0.218 1 | 0.593 1 | 0.455 1 | 0.469 1 | 0.708 1 | 0.210 1 | 0.592 2 | 0.108 10 | 0.000 1 | 0.728 1 | 0.682 2 | 0.671 4 | 0.000 1 | 0.000 6 | 0.407 1 | 0.136 1 | 0.022 2 | 0.575 1 | 0.436 4 | 0.259 1 | 0.428 1 | 0.048 2 | 0.000 1 | 0.000 1 | 0.879 5 | 0.000 1 | 0.480 1 | 0.000 1 | 0.133 4 | 0.597 1 | 0.000 1 | 0.690 1 | 0.000 1 | 0.000 1 | 0.009 10 | 0.000 9 | 0.921 2 | 0.000 5 | 0.151 1 | 0.000 1 | 0.000 5 | 0.000 1 | 0.109 6 | 0.494 8 | 0.622 2 | 0.394 6 | 0.073 9 | 0.141 7 | 0.798 1 | 0.528 2 | 0.026 1 | 0.000 1 | 0.551 2 | 0.000 2 | 0.000 2 | 0.134 5 | 0.717 4 | 0.000 2 | 0.000 1 | 0.000 1 | 0.188 2 | 0.000 4 | 0.000 2 | 0.791 1 | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PonderV2 ScanNet200 | 0.346 2 | 0.552 4 | 0.270 4 | 0.175 3 | 0.810 4 | 0.682 4 | 0.950 2 | 0.560 4 | 0.641 6 | 0.761 1 | 0.398 7 | 0.357 6 | 0.570 5 | 0.113 2 | 0.804 3 | 0.603 3 | 0.750 3 | 0.283 2 | 0.681 4 | 0.952 2 | 0.548 2 | 0.874 3 | 0.852 7 | 0.290 6 | 0.700 2 | 0.356 7 | 0.792 3 | 0.445 6 | 0.545 7 | 0.436 6 | 0.351 7 | 0.787 5 | 0.611 5 | 0.050 6 | 0.290 8 | 0.519 8 | 0.000 1 | 0.825 4 | 0.888 2 | 0.842 3 | 0.259 2 | 0.100 2 | 0.558 3 | 0.070 8 | 0.497 5 | 0.247 8 | 0.457 7 | 0.889 2 | 0.248 5 | 0.106 6 | 0.817 7 | 0.691 3 | 0.094 4 | 0.729 1 | 0.636 3 | 0.620 8 | 0.503 7 | 0.660 9 | 0.243 4 | 0.000 3 | 0.212 5 | 0.590 3 | 0.860 6 | 0.400 3 | 0.881 3 | 0.000 3 | 0.202 1 | 0.622 6 | 0.408 5 | 0.499 6 | 0.261 6 | 0.000 1 | 0.385 5 | 0.636 5 | 0.000 4 | 0.000 6 | 0.000 1 | 0.000 3 | 0.433 11 | 0.843 4 | 0.660 3 | 0.574 8 | 0.481 2 | 0.336 3 | 0.677 3 | 0.486 2 | 0.000 3 | 0.030 1 | 0.000 1 | 0.034 4 | 0.000 3 | 0.080 5 | 0.869 7 | 0.000 1 | 0.000 7 | 0.000 7 | 0.540 4 | 0.727 2 | 0.232 11 | 0.115 5 | 0.186 5 | 0.193 5 | 0.000 10 | 0.403 6 | 0.326 3 | 0.103 8 | 0.000 3 | 0.290 3 | 0.392 6 | 0.000 1 | 0.346 4 | 0.062 7 | 0.424 2 | 0.375 4 | 0.431 3 | 0.667 2 | 0.115 8 | 0.082 7 | 0.239 4 | 0.000 1 | 0.504 8 | 0.606 4 | 0.584 6 | 0.000 1 | 0.002 4 | 0.186 4 | 0.104 6 | 0.000 5 | 0.394 2 | 0.384 6 | 0.083 4 | 0.000 4 | 0.007 5 | 0.000 1 | 0.000 1 | 0.880 4 | 0.000 1 | 0.377 6 | 0.000 1 | 0.263 2 | 0.565 2 | 0.000 1 | 0.608 6 | 0.000 1 | 0.000 1 | 0.304 4 | 0.009 5 | 0.924 1 | 0.000 5 | 0.000 5 | 0.000 1 | 0.000 5 | 0.000 1 | 0.128 2 | 0.584 1 | 0.475 4 | 0.412 5 | 0.076 8 | 0.269 3 | 0.621 3 | 0.509 3 | 0.010 3 | 0.000 1 | 0.491 6 | 0.063 1 | 0.000 2 | 0.472 3 | 0.880 1 | 0.000 2 | 0.000 1 | 0.000 1 | 0.179 3 | 0.125 1 | 0.000 2 | 0.441 5 | 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 6 | 0.556 3 | 0.270 3 | 0.123 8 | 0.816 3 | 0.682 4 | 0.946 3 | 0.549 5 | 0.657 5 | 0.756 3 | 0.459 4 | 0.376 5 | 0.550 6 | 0.001 8 | 0.807 2 | 0.616 1 | 0.727 6 | 0.267 4 | 0.691 3 | 0.942 6 | 0.530 6 | 0.872 4 | 0.874 4 | 0.330 4 | 0.542 8 | 0.374 5 | 0.792 3 | 0.400 8 | 0.673 2 | 0.572 4 | 0.433 1 | 0.793 4 | 0.623 4 | 0.008 11 | 0.351 4 | 0.594 6 | 0.000 1 | 0.783 7 | 0.876 4 | 0.833 4 | 0.213 3 | 0.000 7 | 0.537 4 | 0.091 3 | 0.519 2 | 0.304 4 | 0.620 5 | 0.942 1 | 0.264 2 | 0.124 4 | 0.855 3 | 0.695 2 | 0.086 5 | 0.646 5 | 0.506 10 | 0.658 4 | 0.535 3 | 0.715 2 | 0.314 1 | 0.000 3 | 0.241 3 | 0.608 2 | 0.897 2 | 0.359 5 | 0.858 5 | 0.000 3 | 0.076 11 | 0.611 7 | 0.392 6 | 0.509 5 | 0.378 2 | 0.000 1 | 0.579 2 | 0.565 10 | 0.000 4 | 0.000 6 | 0.000 1 | 0.000 3 | 0.755 4 | 0.806 7 | 0.661 1 | 0.572 9 | 0.350 6 | 0.181 6 | 0.660 6 | 0.300 8 | 0.000 3 | 0.000 4 | 0.000 1 | 0.023 6 | 0.000 3 | 0.042 10 | 0.930 2 | 0.000 1 | 0.000 7 | 0.077 4 | 0.584 3 | 0.392 6 | 0.339 6 | 0.185 4 | 0.171 7 | 0.308 2 | 0.006 9 | 0.563 3 | 0.256 5 | 0.150 1 | 0.000 3 | 0.002 10 | 0.345 9 | 0.000 1 | 0.045 8 | 0.197 2 | 0.063 5 | 0.323 8 | 0.453 2 | 0.600 5 | 0.163 6 | 0.037 9 | 0.349 2 | 0.000 1 | 0.672 2 | 0.679 3 | 0.753 1 | 0.000 1 | 0.000 6 | 0.000 6 | 0.117 2 | 0.000 5 | 0.000 6 | 0.291 8 | 0.000 6 | 0.000 4 | 0.039 3 | 0.000 1 | 0.000 1 | 0.899 2 | 0.000 1 | 0.374 7 | 0.000 1 | 0.000 7 | 0.545 4 | 0.000 1 | 0.634 3 | 0.000 1 | 0.000 1 | 0.074 7 | 0.223 3 | 0.914 5 | 0.000 5 | 0.021 3 | 0.000 1 | 0.000 5 | 0.000 1 | 0.112 4 | 0.498 7 | 0.649 1 | 0.383 7 | 0.095 1 | 0.135 10 | 0.449 7 | 0.432 6 | 0.008 5 | 0.000 1 | 0.518 4 | 0.000 2 | 0.000 2 | 0.000 7 | 0.796 2 | 0.000 2 | 0.000 1 | 0.000 1 | 0.138 8 | 0.000 4 | 0.000 2 | 0.000 6 | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
LGround | ![]() | 0.272 9 | 0.485 9 | 0.184 9 | 0.106 9 | 0.778 9 | 0.676 6 | 0.932 9 | 0.479 11 | 0.572 9 | 0.718 8 | 0.399 6 | 0.265 9 | 0.453 10 | 0.085 3 | 0.745 9 | 0.446 9 | 0.726 7 | 0.232 9 | 0.622 9 | 0.901 9 | 0.512 8 | 0.826 9 | 0.786 10 | 0.178 10 | 0.549 7 | 0.277 9 | 0.659 9 | 0.381 9 | 0.518 8 | 0.295 11 | 0.323 8 | 0.777 7 | 0.599 7 | 0.028 7 | 0.321 5 | 0.363 10 | 0.000 1 | 0.708 9 | 0.858 9 | 0.746 8 | 0.063 9 | 0.022 5 | 0.457 9 | 0.077 6 | 0.476 6 | 0.243 9 | 0.402 8 | 0.397 11 | 0.233 6 | 0.077 11 | 0.720 11 | 0.610 10 | 0.103 3 | 0.629 7 | 0.437 11 | 0.626 7 | 0.446 8 | 0.702 4 | 0.190 7 | 0.005 1 | 0.058 10 | 0.322 9 | 0.702 10 | 0.244 9 | 0.768 8 | 0.000 3 | 0.134 7 | 0.552 9 | 0.279 10 | 0.395 9 | 0.147 10 | 0.000 1 | 0.207 9 | 0.612 8 | 0.000 4 | 0.000 6 | 0.000 1 | 0.000 3 | 0.658 6 | 0.566 9 | 0.323 9 | 0.525 11 | 0.229 8 | 0.179 7 | 0.467 11 | 0.154 10 | 0.000 3 | 0.002 2 | 0.000 1 | 0.051 1 | 0.000 3 | 0.127 2 | 0.703 9 | 0.000 1 | 0.000 7 | 0.216 1 | 0.112 10 | 0.358 7 | 0.547 1 | 0.187 3 | 0.092 10 | 0.156 11 | 0.055 7 | 0.296 9 | 0.252 6 | 0.143 2 | 0.000 3 | 0.014 8 | 0.398 5 | 0.000 1 | 0.028 10 | 0.173 4 | 0.000 9 | 0.265 10 | 0.348 9 | 0.415 10 | 0.179 3 | 0.019 10 | 0.218 5 | 0.000 1 | 0.597 5 | 0.274 11 | 0.565 7 | 0.000 1 | 0.012 3 | 0.000 6 | 0.039 10 | 0.022 2 | 0.000 6 | 0.117 9 | 0.000 6 | 0.000 4 | 0.000 7 | 0.000 1 | 0.000 1 | 0.324 10 | 0.000 1 | 0.384 5 | 0.000 1 | 0.000 7 | 0.251 11 | 0.000 1 | 0.566 8 | 0.000 1 | 0.000 1 | 0.066 8 | 0.404 1 | 0.886 9 | 0.199 1 | 0.000 5 | 0.000 1 | 0.059 3 | 0.000 1 | 0.136 1 | 0.540 3 | 0.127 11 | 0.295 8 | 0.085 5 | 0.143 6 | 0.514 4 | 0.413 9 | 0.000 8 | 0.000 1 | 0.498 5 | 0.000 2 | 0.000 2 | 0.000 7 | 0.623 7 | 0.000 2 | 0.000 1 | 0.000 1 | 0.132 10 | 0.000 4 | 0.000 2 | 0.000 6 | 0.000 1 | ||||||||||||||||||||||||||||
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
OctFormer ScanNet200 | ![]() | 0.326 7 | 0.539 6 | 0.265 6 | 0.131 6 | 0.806 5 | 0.670 7 | 0.943 6 | 0.535 7 | 0.662 2 | 0.705 10 | 0.423 5 | 0.407 3 | 0.505 8 | 0.003 7 | 0.765 7 | 0.582 4 | 0.686 9 | 0.227 10 | 0.680 5 | 0.943 5 | 0.601 1 | 0.854 8 | 0.892 2 | 0.335 2 | 0.417 11 | 0.357 6 | 0.724 7 | 0.453 5 | 0.632 4 | 0.596 2 | 0.432 2 | 0.783 6 | 0.512 11 | 0.021 9 | 0.244 9 | 0.637 1 | 0.000 1 | 0.787 6 | 0.873 6 | 0.743 9 | 0.000 11 | 0.000 7 | 0.534 5 | 0.110 1 | 0.499 4 | 0.289 5 | 0.626 4 | 0.620 9 | 0.168 11 | 0.204 1 | 0.849 4 | 0.679 4 | 0.117 2 | 0.633 6 | 0.684 2 | 0.650 5 | 0.552 2 | 0.684 7 | 0.312 2 | 0.000 3 | 0.175 6 | 0.429 6 | 0.865 3 | 0.413 2 | 0.837 6 | 0.000 3 | 0.145 5 | 0.626 5 | 0.451 4 | 0.487 7 | 0.513 1 | 0.000 1 | 0.529 4 | 0.613 7 | 0.000 4 | 0.033 3 | 0.000 1 | 0.000 3 | 0.828 2 | 0.871 2 | 0.622 5 | 0.587 5 | 0.411 4 | 0.137 8 | 0.645 8 | 0.343 6 | 0.000 3 | 0.000 4 | 0.000 1 | 0.022 7 | 0.000 3 | 0.026 11 | 0.829 8 | 0.000 1 | 0.022 5 | 0.089 3 | 0.842 1 | 0.253 10 | 0.318 8 | 0.296 2 | 0.178 6 | 0.291 3 | 0.224 1 | 0.584 2 | 0.200 8 | 0.132 5 | 0.000 3 | 0.128 5 | 0.227 10 | 0.000 1 | 0.230 7 | 0.047 8 | 0.149 4 | 0.331 7 | 0.412 6 | 0.618 4 | 0.164 5 | 0.102 6 | 0.522 1 | 0.000 1 | 0.655 3 | 0.378 7 | 0.469 9 | 0.000 1 | 0.000 6 | 0.000 6 | 0.105 5 | 0.000 5 | 0.000 6 | 0.483 3 | 0.000 6 | 0.000 4 | 0.028 4 | 0.000 1 | 0.000 1 | 0.906 1 | 0.000 1 | 0.339 9 | 0.000 1 | 0.000 7 | 0.457 6 | 0.000 1 | 0.612 5 | 0.000 1 | 0.000 1 | 0.408 2 | 0.000 9 | 0.900 6 | 0.000 5 | 0.000 5 | 0.000 1 | 0.029 4 | 0.000 1 | 0.074 11 | 0.455 9 | 0.479 3 | 0.427 4 | 0.079 7 | 0.140 8 | 0.496 5 | 0.414 8 | 0.022 2 | 0.000 1 | 0.471 8 | 0.000 2 | 0.000 2 | 0.000 7 | 0.722 3 | 0.000 2 | 0.000 1 | 0.000 1 | 0.138 8 | 0.000 4 | 0.000 2 | 0.000 6 | 0.000 1 | ||||||||||||||||||||||||||||
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CeCo | 0.340 3 | 0.551 5 | 0.247 7 | 0.181 2 | 0.784 7 | 0.661 8 | 0.939 7 | 0.564 3 | 0.624 7 | 0.721 6 | 0.484 3 | 0.429 2 | 0.575 2 | 0.027 5 | 0.774 6 | 0.503 8 | 0.753 2 | 0.242 7 | 0.656 7 | 0.945 4 | 0.534 4 | 0.865 5 | 0.860 5 | 0.177 11 | 0.616 5 | 0.400 2 | 0.818 2 | 0.579 1 | 0.615 5 | 0.367 8 | 0.408 4 | 0.726 9 | 0.633 2 | 0.162 1 | 0.360 3 | 0.619 2 | 0.000 1 | 0.828 3 | 0.873 6 | 0.924 2 | 0.109 8 | 0.083 3 | 0.564 2 | 0.057 11 | 0.475 7 | 0.266 6 | 0.781 1 | 0.767 6 | 0.257 4 | 0.100 7 | 0.825 5 | 0.663 6 | 0.048 10 | 0.620 8 | 0.551 6 | 0.595 9 | 0.532 4 | 0.692 6 | 0.246 3 | 0.000 3 | 0.213 4 | 0.615 1 | 0.861 5 | 0.376 4 | 0.900 2 | 0.000 3 | 0.102 10 | 0.660 4 | 0.321 9 | 0.547 3 | 0.226 7 | 0.000 1 | 0.311 7 | 0.742 2 | 0.011 3 | 0.006 5 | 0.000 1 | 0.000 3 | 0.546 10 | 0.824 6 | 0.345 8 | 0.665 1 | 0.450 3 | 0.435 1 | 0.683 2 | 0.411 4 | 0.338 1 | 0.000 4 | 0.000 1 | 0.030 5 | 0.000 3 | 0.068 6 | 0.892 5 | 0.000 1 | 0.063 3 | 0.000 7 | 0.257 7 | 0.304 9 | 0.387 3 | 0.079 8 | 0.228 3 | 0.190 6 | 0.000 10 | 0.586 1 | 0.347 2 | 0.133 4 | 0.000 3 | 0.037 7 | 0.377 7 | 0.000 1 | 0.384 3 | 0.006 10 | 0.003 7 | 0.421 2 | 0.410 7 | 0.643 3 | 0.171 4 | 0.121 4 | 0.142 8 | 0.000 1 | 0.510 7 | 0.447 6 | 0.474 8 | 0.000 1 | 0.000 6 | 0.286 2 | 0.083 7 | 0.000 5 | 0.000 6 | 0.603 1 | 0.096 3 | 0.063 3 | 0.000 7 | 0.000 1 | 0.000 1 | 0.898 3 | 0.000 1 | 0.429 3 | 0.000 1 | 0.400 1 | 0.550 3 | 0.000 1 | 0.633 4 | 0.000 1 | 0.000 1 | 0.377 3 | 0.000 9 | 0.916 3 | 0.000 5 | 0.000 5 | 0.000 1 | 0.000 5 | 0.000 1 | 0.102 9 | 0.499 6 | 0.296 8 | 0.463 3 | 0.089 4 | 0.304 1 | 0.740 2 | 0.401 10 | 0.010 3 | 0.000 1 | 0.560 1 | 0.000 2 | 0.000 2 | 0.709 1 | 0.652 6 | 0.000 2 | 0.000 1 | 0.000 1 | 0.143 6 | 0.000 4 | 0.000 2 | 0.609 2 | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CSC-Pretrain | ![]() | 0.249 11 | 0.455 11 | 0.171 10 | 0.079 11 | 0.766 11 | 0.659 9 | 0.930 11 | 0.494 8 | 0.542 11 | 0.700 11 | 0.314 11 | 0.215 11 | 0.430 11 | 0.121 1 | 0.697 11 | 0.441 10 | 0.683 10 | 0.235 8 | 0.609 11 | 0.895 10 | 0.476 11 | 0.816 10 | 0.770 11 | 0.186 8 | 0.634 4 | 0.216 11 | 0.734 6 | 0.340 10 | 0.471 10 | 0.307 10 | 0.293 11 | 0.591 11 | 0.542 10 | 0.076 5 | 0.205 10 | 0.464 9 | 0.000 1 | 0.484 11 | 0.832 11 | 0.766 6 | 0.052 10 | 0.000 7 | 0.413 10 | 0.059 10 | 0.418 10 | 0.222 10 | 0.318 11 | 0.609 10 | 0.206 9 | 0.112 5 | 0.743 8 | 0.625 8 | 0.076 6 | 0.579 10 | 0.548 7 | 0.590 10 | 0.371 10 | 0.552 11 | 0.081 10 | 0.003 2 | 0.142 8 | 0.201 11 | 0.638 11 | 0.233 10 | 0.686 11 | 0.000 3 | 0.142 6 | 0.444 11 | 0.375 7 | 0.247 11 | 0.198 8 | 0.000 1 | 0.128 11 | 0.454 11 | 0.019 2 | 0.097 1 | 0.000 1 | 0.000 3 | 0.553 9 | 0.557 10 | 0.373 7 | 0.545 10 | 0.164 10 | 0.014 11 | 0.547 10 | 0.174 9 | 0.000 3 | 0.002 2 | 0.000 1 | 0.037 2 | 0.000 3 | 0.063 8 | 0.664 11 | 0.000 1 | 0.000 7 | 0.130 2 | 0.170 8 | 0.152 11 | 0.335 7 | 0.079 8 | 0.110 9 | 0.175 8 | 0.098 6 | 0.175 11 | 0.166 9 | 0.045 11 | 0.207 1 | 0.014 8 | 0.465 3 | 0.000 1 | 0.001 11 | 0.001 11 | 0.046 6 | 0.299 9 | 0.327 10 | 0.537 7 | 0.033 10 | 0.012 11 | 0.186 7 | 0.000 1 | 0.205 10 | 0.377 8 | 0.463 10 | 0.000 1 | 0.058 2 | 0.000 6 | 0.055 9 | 0.041 1 | 0.000 6 | 0.105 10 | 0.000 6 | 0.000 4 | 0.000 7 | 0.000 1 | 0.000 1 | 0.398 9 | 0.000 1 | 0.308 11 | 0.000 1 | 0.000 7 | 0.319 9 | 0.000 1 | 0.543 9 | 0.000 1 | 0.000 1 | 0.062 9 | 0.004 7 | 0.862 10 | 0.000 5 | 0.000 5 | 0.000 1 | 0.000 5 | 0.000 1 | 0.123 3 | 0.316 10 | 0.225 9 | 0.250 9 | 0.094 2 | 0.180 5 | 0.332 10 | 0.441 5 | 0.000 8 | 0.000 1 | 0.310 11 | 0.000 2 | 0.000 2 | 0.000 7 | 0.592 8 | 0.000 2 | 0.000 1 | 0.000 1 | 0.203 1 | 0.000 4 | 0.000 2 | 0.000 6 | 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 10 | 0.463 10 | 0.154 11 | 0.102 10 | 0.771 10 | 0.650 10 | 0.932 9 | 0.483 10 | 0.571 10 | 0.710 9 | 0.331 10 | 0.250 10 | 0.492 9 | 0.044 4 | 0.703 10 | 0.419 11 | 0.606 11 | 0.227 10 | 0.621 10 | 0.865 11 | 0.531 5 | 0.771 11 | 0.813 8 | 0.291 5 | 0.484 9 | 0.242 10 | 0.612 11 | 0.282 11 | 0.440 11 | 0.351 9 | 0.299 9 | 0.622 10 | 0.593 8 | 0.027 8 | 0.293 7 | 0.310 11 | 0.000 1 | 0.757 8 | 0.858 9 | 0.737 10 | 0.150 5 | 0.164 1 | 0.368 11 | 0.084 4 | 0.381 11 | 0.142 11 | 0.357 9 | 0.720 7 | 0.214 8 | 0.092 10 | 0.724 10 | 0.596 11 | 0.056 9 | 0.655 4 | 0.525 8 | 0.581 11 | 0.352 11 | 0.594 10 | 0.056 11 | 0.000 3 | 0.014 11 | 0.224 10 | 0.772 9 | 0.205 11 | 0.720 10 | 0.000 3 | 0.159 4 | 0.531 10 | 0.163 11 | 0.294 10 | 0.136 11 | 0.000 1 | 0.169 10 | 0.589 9 | 0.000 4 | 0.000 6 | 0.000 1 | 0.002 1 | 0.663 5 | 0.466 11 | 0.265 11 | 0.582 6 | 0.337 7 | 0.016 10 | 0.559 9 | 0.084 11 | 0.000 3 | 0.000 4 | 0.000 1 | 0.036 3 | 0.000 3 | 0.125 3 | 0.670 10 | 0.000 1 | 0.102 1 | 0.071 5 | 0.164 9 | 0.406 5 | 0.386 4 | 0.046 10 | 0.068 11 | 0.159 9 | 0.117 3 | 0.284 10 | 0.111 10 | 0.094 10 | 0.000 3 | 0.000 11 | 0.197 11 | 0.000 1 | 0.044 9 | 0.013 9 | 0.002 8 | 0.228 11 | 0.307 11 | 0.588 6 | 0.025 11 | 0.545 3 | 0.134 9 | 0.000 1 | 0.655 3 | 0.302 9 | 0.282 11 | 0.000 1 | 0.060 1 | 0.000 6 | 0.035 11 | 0.000 5 | 0.000 6 | 0.097 11 | 0.000 6 | 0.000 4 | 0.005 6 | 0.000 1 | 0.000 1 | 0.096 11 | 0.000 1 | 0.334 10 | 0.000 1 | 0.000 7 | 0.274 10 | 0.000 1 | 0.513 11 | 0.000 1 | 0.000 1 | 0.280 5 | 0.194 4 | 0.897 7 | 0.000 5 | 0.000 5 | 0.000 1 | 0.000 5 | 0.000 1 | 0.108 7 | 0.279 11 | 0.189 10 | 0.141 11 | 0.059 10 | 0.272 2 | 0.307 11 | 0.445 4 | 0.003 6 | 0.000 1 | 0.353 10 | 0.000 2 | 0.026 1 | 0.000 7 | 0.581 9 | 0.001 1 | 0.000 1 | 0.000 1 | 0.093 11 | 0.002 3 | 0.000 2 | 0.000 6 | 0.000 1 | ||||||||||||||||||||||||||||
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
AWCS | 0.305 8 | 0.508 8 | 0.225 8 | 0.142 5 | 0.782 8 | 0.634 11 | 0.937 8 | 0.489 9 | 0.578 8 | 0.721 6 | 0.364 9 | 0.355 7 | 0.515 7 | 0.023 6 | 0.764 8 | 0.523 7 | 0.707 8 | 0.264 5 | 0.633 8 | 0.922 8 | 0.507 9 | 0.886 1 | 0.804 9 | 0.179 9 | 0.436 10 | 0.300 8 | 0.656 10 | 0.529 2 | 0.501 9 | 0.394 7 | 0.296 10 | 0.820 2 | 0.603 6 | 0.131 3 | 0.179 11 | 0.619 2 | 0.000 1 | 0.707 10 | 0.865 8 | 0.773 5 | 0.171 4 | 0.010 6 | 0.484 8 | 0.063 9 | 0.463 8 | 0.254 7 | 0.332 10 | 0.649 8 | 0.220 7 | 0.100 7 | 0.729 9 | 0.613 9 | 0.071 8 | 0.582 9 | 0.628 4 | 0.702 2 | 0.424 9 | 0.749 1 | 0.137 9 | 0.000 3 | 0.142 8 | 0.360 8 | 0.863 4 | 0.305 8 | 0.877 4 | 0.000 3 | 0.173 2 | 0.606 8 | 0.337 8 | 0.478 8 | 0.154 9 | 0.000 1 | 0.253 8 | 0.664 4 | 0.000 4 | 0.000 6 | 0.000 1 | 0.000 3 | 0.626 8 | 0.782 8 | 0.302 10 | 0.602 3 | 0.185 9 | 0.282 5 | 0.651 7 | 0.317 7 | 0.000 3 | 0.000 4 | 0.000 1 | 0.022 7 | 0.000 3 | 0.154 1 | 0.876 6 | 0.000 1 | 0.014 6 | 0.063 6 | 0.029 11 | 0.553 3 | 0.467 2 | 0.084 7 | 0.124 8 | 0.157 10 | 0.049 8 | 0.373 8 | 0.252 6 | 0.097 9 | 0.000 3 | 0.219 4 | 0.542 2 | 0.000 1 | 0.392 2 | 0.172 5 | 0.000 9 | 0.339 6 | 0.417 5 | 0.533 8 | 0.093 9 | 0.115 5 | 0.195 6 | 0.000 1 | 0.516 6 | 0.288 10 | 0.741 2 | 0.000 1 | 0.001 5 | 0.233 3 | 0.056 8 | 0.000 5 | 0.159 3 | 0.334 7 | 0.077 5 | 0.000 4 | 0.000 7 | 0.000 1 | 0.000 1 | 0.749 8 | 0.000 1 | 0.411 4 | 0.000 1 | 0.008 6 | 0.452 7 | 0.000 1 | 0.595 7 | 0.000 1 | 0.000 1 | 0.220 6 | 0.006 6 | 0.894 8 | 0.006 4 | 0.000 5 | 0.000 1 | 0.000 5 | 0.000 1 | 0.112 4 | 0.504 5 | 0.404 6 | 0.551 1 | 0.093 3 | 0.129 11 | 0.484 6 | 0.381 11 | 0.000 8 | 0.000 1 | 0.396 9 | 0.000 2 | 0.000 2 | 0.620 2 | 0.402 11 | 0.000 2 | 0.000 1 | 0.000 1 | 0.142 7 | 0.000 4 | 0.000 2 | 0.512 4 | 0.000 1 | |||||||||||||||||||||||||||||