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 | alarm clock | armchair | backpack | bag | ball | bar | basket | bathroom cabinet | bathroom counter | bathroom stall | bathroom stall door | bathroom vanity | bathtub | bed | bench | bicycle | bin | blackboard | blanket | blinds | board | book | bookshelf | bottle | bowl | box | broom | bucket | bulletin board | cabinet | calendar | candle | cart | case of water bottles | cd case | ceiling | ceiling light | chair | clock | closet | closet door | closet rod | closet wall | clothes | clothes dryer | coat rack | coffee kettle | coffee maker | coffee table | column | computer tower | container | copier | couch | counter | crate | cup | curtain | cushion | decoration | desk | dining table | dish rack | dishwasher | divider | door | doorframe | dresser | dumbbell | dustpan | end table | fan | file cabinet | fire alarm | fire extinguisher | fireplace | floor | folded chair | furniture | guitar | guitar case | hair dryer | handicap bar | hat | headphones | ironing board | jacket | keyboard | keyboard piano | kitchen cabinet | kitchen counter | ladder | lamp | laptop | laundry basket | laundry detergent | laundry hamper | ledge | light | light switch | luggage | machine | mailbox | mat | mattress | microwave | mini fridge | mirror | monitor | mouse | music stand | nightstand | object | office chair | ottoman | oven | paper | paper bag | paper cutter | paper towel dispenser | paper towel roll | person | piano | picture | pillar | pillow | pipe | plant | plate | plunger | poster | potted plant | power outlet | power strip | printer | projector | projector screen | purse | rack | radiator | rail | range hood | recycling bin | refrigerator | scale | seat | shelf | shoe | shower | shower curtain | shower curtain rod | shower door | shower floor | shower head | shower wall | sign | sink | soap dish | soap dispenser | sofa chair | speaker | stair rail | stairs | stand | stool | storage bin | storage container | storage organizer | stove | structure | stuffed animal | suitcase | table | telephone | tissue box | toaster | toaster oven | toilet | toilet paper | toilet paper dispenser | toilet paper holder | toilet seat cover dispenser | towel | trash bin | trash can | tray | tube | tv | tv stand | vacuum cleaner | vent | wall | wardrobe | washing machine | water bottle | water cooler | water pitcher | whiteboard | window | windowsill |
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PonderV2 ScanNet200 | 0.346 2 | 0.552 4 | 0.270 4 | 0.175 3 | 0.497 5 | 0.070 8 | 0.239 4 | 0.000 1 | 0.000 3 | 0.000 1 | 0.232 11 | 0.412 5 | 0.584 1 | 0.842 3 | 0.804 3 | 0.212 5 | 0.540 4 | 0.000 3 | 0.433 11 | 0.106 6 | 0.000 6 | 0.590 3 | 0.290 6 | 0.548 2 | 0.243 4 | 0.000 5 | 0.356 7 | 0.000 1 | 0.000 3 | 0.062 7 | 0.398 7 | 0.441 5 | 0.000 1 | 0.104 6 | 0.000 2 | 0.888 2 | 0.076 8 | 0.682 4 | 0.030 1 | 0.094 4 | 0.491 6 | 0.351 7 | 0.869 7 | 0.000 1 | 0.063 1 | 0.403 6 | 0.700 2 | 0.000 7 | 0.660 9 | 0.881 3 | 0.761 1 | 0.050 6 | 0.186 5 | 0.852 7 | 0.000 1 | 0.007 5 | 0.570 5 | 0.100 2 | 0.565 2 | 0.326 3 | 0.641 6 | 0.431 3 | 0.290 8 | 0.621 3 | 0.259 2 | 0.408 5 | 0.622 6 | 0.125 1 | 0.082 7 | 0.950 2 | 0.179 3 | 0.000 1 | 0.263 2 | 0.424 2 | 0.193 5 | 0.558 3 | 0.880 1 | 0.545 7 | 0.375 4 | 0.727 2 | 0.445 6 | 0.499 6 | 0.000 3 | 0.000 1 | 0.475 4 | 0.002 4 | 0.034 4 | 0.083 4 | 0.000 3 | 0.924 1 | 0.290 3 | 0.636 3 | 0.115 8 | 0.400 3 | 0.874 3 | 0.186 4 | 0.000 1 | 0.611 5 | 0.128 2 | 0.113 2 | 0.000 4 | 0.000 1 | 0.000 5 | 0.584 6 | 0.636 5 | 0.103 8 | 0.385 5 | 0.843 4 | 0.283 2 | 0.603 3 | 0.080 5 | 0.825 4 | 0.000 3 | 0.377 6 | 0.000 1 | 0.000 4 | 0.000 2 | 0.457 7 | 0.000 1 | 0.000 5 | 0.000 1 | 0.574 8 | 0.608 6 | 0.481 2 | 0.792 3 | 0.394 2 | 0.000 1 | 0.357 6 | 0.503 7 | 0.261 6 | 0.817 7 | 0.504 8 | 0.304 4 | 0.472 3 | 0.115 5 | 0.000 1 | 0.750 3 | 0.677 3 | 0.202 1 | 0.000 7 | 0.509 3 | 0.729 1 | 0.000 1 | 0.519 8 | 0.000 10 | 0.000 5 | 0.000 1 | 0.620 8 | 0.000 2 | 0.000 1 | 0.660 3 | 0.560 4 | 0.486 2 | 0.384 6 | 0.346 4 | 0.952 2 | 0.247 8 | 0.667 2 | 0.436 6 | 0.269 3 | 0.691 3 | 0.000 1 | 0.010 3 | 0.787 5 | 0.889 2 | 0.880 4 | 0.000 1 | 0.810 4 | 0.336 3 | 0.860 6 | 0.000 1 | 0.606 4 | 0.009 5 | 0.248 5 | 0.681 4 | 0.392 6 | |||||||||||||||||||||||||||||
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PTv3 ScanNet200 | 0.393 1 | 0.592 1 | 0.330 1 | 0.216 1 | 0.520 1 | 0.109 2 | 0.108 10 | 0.000 1 | 0.337 1 | 0.000 1 | 0.310 9 | 0.394 6 | 0.494 8 | 0.753 7 | 0.848 1 | 0.256 2 | 0.717 2 | 0.000 3 | 0.842 1 | 0.192 2 | 0.065 2 | 0.449 5 | 0.346 1 | 0.546 3 | 0.190 7 | 0.000 5 | 0.384 4 | 0.000 1 | 0.000 3 | 0.218 1 | 0.505 1 | 0.791 1 | 0.000 1 | 0.136 1 | 0.000 2 | 0.903 1 | 0.073 9 | 0.687 3 | 0.000 4 | 0.168 1 | 0.551 2 | 0.387 5 | 0.941 1 | 0.000 1 | 0.000 2 | 0.397 7 | 0.654 3 | 0.000 7 | 0.714 3 | 0.759 9 | 0.752 4 | 0.118 4 | 0.264 2 | 0.926 1 | 0.000 1 | 0.048 2 | 0.575 2 | 0.000 7 | 0.597 1 | 0.366 1 | 0.755 1 | 0.469 1 | 0.474 1 | 0.798 1 | 0.140 6 | 0.617 1 | 0.692 3 | 0.000 4 | 0.592 2 | 0.971 1 | 0.188 2 | 0.000 1 | 0.133 4 | 0.593 1 | 0.349 1 | 0.650 1 | 0.717 4 | 0.699 1 | 0.455 1 | 0.790 1 | 0.523 3 | 0.636 1 | 0.301 1 | 0.000 1 | 0.622 2 | 0.000 6 | 0.017 9 | 0.259 1 | 0.000 3 | 0.921 2 | 0.337 1 | 0.733 1 | 0.210 1 | 0.514 1 | 0.860 6 | 0.407 1 | 0.000 1 | 0.688 1 | 0.109 6 | 0.000 9 | 0.000 4 | 0.000 1 | 0.151 1 | 0.671 4 | 0.782 1 | 0.115 7 | 0.641 1 | 0.903 1 | 0.349 1 | 0.616 1 | 0.088 4 | 0.832 2 | 0.000 3 | 0.480 1 | 0.000 1 | 0.428 1 | 0.000 2 | 0.497 6 | 0.000 1 | 0.000 5 | 0.000 1 | 0.662 2 | 0.690 1 | 0.612 1 | 0.828 1 | 0.575 1 | 0.000 1 | 0.404 4 | 0.644 1 | 0.325 3 | 0.887 2 | 0.728 1 | 0.009 10 | 0.134 5 | 0.026 11 | 0.000 1 | 0.761 1 | 0.731 1 | 0.172 3 | 0.077 2 | 0.528 2 | 0.727 2 | 0.000 1 | 0.603 4 | 0.220 2 | 0.022 2 | 0.000 1 | 0.740 1 | 0.000 2 | 0.000 1 | 0.661 1 | 0.586 1 | 0.566 1 | 0.436 4 | 0.531 1 | 0.978 1 | 0.457 1 | 0.708 1 | 0.583 3 | 0.141 7 | 0.748 1 | 0.000 1 | 0.026 1 | 0.822 1 | 0.871 3 | 0.879 5 | 0.000 1 | 0.851 1 | 0.405 2 | 0.914 1 | 0.000 1 | 0.682 2 | 0.000 9 | 0.281 1 | 0.738 1 | 0.463 4 | |||||||||||||||||||||||||||||
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) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
L3DETR-ScanNet_200 | 0.336 4 | 0.533 7 | 0.279 2 | 0.155 4 | 0.508 3 | 0.073 7 | 0.101 11 | 0.000 1 | 0.058 2 | 0.000 1 | 0.294 10 | 0.233 10 | 0.548 2 | 0.927 1 | 0.788 5 | 0.264 1 | 0.463 5 | 0.000 3 | 0.638 7 | 0.098 9 | 0.014 4 | 0.411 7 | 0.226 7 | 0.525 7 | 0.225 6 | 0.010 3 | 0.397 3 | 0.000 1 | 0.000 3 | 0.192 3 | 0.380 8 | 0.598 3 | 0.000 1 | 0.117 2 | 0.000 2 | 0.883 3 | 0.082 6 | 0.689 2 | 0.000 4 | 0.032 11 | 0.549 3 | 0.417 3 | 0.910 3 | 0.000 1 | 0.000 2 | 0.448 5 | 0.613 6 | 0.000 7 | 0.697 5 | 0.960 1 | 0.759 2 | 0.158 2 | 0.293 1 | 0.883 3 | 0.000 1 | 0.312 1 | 0.583 1 | 0.079 4 | 0.422 8 | 0.068 11 | 0.660 4 | 0.418 4 | 0.298 6 | 0.430 8 | 0.114 7 | 0.526 3 | 0.776 1 | 0.051 2 | 0.679 1 | 0.946 3 | 0.152 5 | 0.000 1 | 0.183 3 | 0.000 9 | 0.211 4 | 0.511 6 | 0.409 10 | 0.565 6 | 0.355 5 | 0.448 4 | 0.512 4 | 0.557 2 | 0.000 3 | 0.000 1 | 0.420 5 | 0.000 6 | 0.007 11 | 0.104 2 | 0.000 3 | 0.125 11 | 0.330 2 | 0.514 9 | 0.146 7 | 0.321 7 | 0.860 6 | 0.174 5 | 0.000 1 | 0.629 3 | 0.075 10 | 0.000 9 | 0.000 4 | 0.000 1 | 0.002 4 | 0.671 4 | 0.712 3 | 0.141 3 | 0.339 6 | 0.856 3 | 0.261 6 | 0.529 6 | 0.067 7 | 0.835 1 | 0.000 3 | 0.369 8 | 0.000 1 | 0.259 2 | 0.000 2 | 0.629 3 | 0.000 1 | 0.487 1 | 0.000 1 | 0.579 7 | 0.646 2 | 0.107 11 | 0.720 8 | 0.122 4 | 0.000 1 | 0.333 8 | 0.505 6 | 0.303 5 | 0.908 1 | 0.503 9 | 0.565 1 | 0.074 6 | 0.324 1 | 0.000 1 | 0.740 4 | 0.661 5 | 0.109 8 | 0.000 7 | 0.427 7 | 0.563 11 | 0.000 1 | 0.579 7 | 0.108 5 | 0.000 5 | 0.000 1 | 0.664 3 | 0.000 2 | 0.000 1 | 0.641 4 | 0.539 6 | 0.416 3 | 0.515 2 | 0.256 5 | 0.940 7 | 0.312 3 | 0.209 11 | 0.620 1 | 0.138 9 | 0.636 7 | 0.000 1 | 0.000 8 | 0.775 8 | 0.861 4 | 0.765 7 | 0.000 1 | 0.801 6 | 0.119 9 | 0.860 6 | 0.000 1 | 0.687 1 | 0.001 8 | 0.192 10 | 0.679 6 | 0.699 1 | |||||||||||||||||||||||||||||
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
OA-CNN-L_ScanNet200 | 0.333 5 | 0.558 2 | 0.269 5 | 0.124 7 | 0.448 9 | 0.080 5 | 0.272 3 | 0.000 1 | 0.000 3 | 0.000 1 | 0.342 5 | 0.515 2 | 0.524 4 | 0.713 11 | 0.789 4 | 0.158 7 | 0.384 6 | 0.000 3 | 0.806 3 | 0.125 3 | 0.000 6 | 0.496 4 | 0.332 3 | 0.498 10 | 0.227 5 | 0.024 2 | 0.474 1 | 0.000 1 | 0.003 2 | 0.071 6 | 0.487 2 | 0.000 6 | 0.000 1 | 0.110 4 | 0.000 2 | 0.876 4 | 0.013 11 | 0.703 1 | 0.000 4 | 0.076 6 | 0.473 7 | 0.355 6 | 0.906 4 | 0.000 1 | 0.000 2 | 0.476 4 | 0.706 1 | 0.000 7 | 0.672 8 | 0.835 7 | 0.748 5 | 0.015 10 | 0.223 4 | 0.860 5 | 0.000 1 | 0.000 7 | 0.572 4 | 0.000 7 | 0.509 5 | 0.313 4 | 0.662 2 | 0.398 8 | 0.396 2 | 0.411 9 | 0.276 1 | 0.527 2 | 0.711 2 | 0.000 4 | 0.076 8 | 0.946 3 | 0.166 4 | 0.000 1 | 0.022 5 | 0.160 3 | 0.183 7 | 0.493 7 | 0.699 5 | 0.637 3 | 0.403 3 | 0.330 8 | 0.406 7 | 0.526 4 | 0.024 2 | 0.000 1 | 0.392 7 | 0.000 6 | 0.016 10 | 0.000 6 | 0.196 2 | 0.915 4 | 0.112 6 | 0.557 5 | 0.197 2 | 0.352 6 | 0.877 2 | 0.000 6 | 0.000 1 | 0.592 9 | 0.103 8 | 0.000 9 | 0.067 1 | 0.000 1 | 0.089 2 | 0.735 3 | 0.625 6 | 0.130 6 | 0.568 3 | 0.836 5 | 0.271 3 | 0.534 5 | 0.043 9 | 0.799 5 | 0.001 2 | 0.445 2 | 0.000 1 | 0.000 4 | 0.024 1 | 0.661 2 | 0.000 1 | 0.262 2 | 0.000 1 | 0.591 4 | 0.517 10 | 0.373 5 | 0.788 5 | 0.021 5 | 0.000 1 | 0.455 1 | 0.517 5 | 0.320 4 | 0.823 6 | 0.200 11 | 0.001 11 | 0.150 4 | 0.100 6 | 0.000 1 | 0.736 5 | 0.668 4 | 0.103 9 | 0.052 4 | 0.662 1 | 0.720 3 | 0.000 1 | 0.602 5 | 0.112 4 | 0.002 4 | 0.000 1 | 0.637 6 | 0.000 2 | 0.000 1 | 0.621 6 | 0.569 2 | 0.398 5 | 0.412 5 | 0.234 6 | 0.949 3 | 0.363 2 | 0.492 9 | 0.495 5 | 0.251 4 | 0.665 5 | 0.000 1 | 0.001 7 | 0.805 3 | 0.833 5 | 0.794 6 | 0.000 1 | 0.821 2 | 0.314 4 | 0.843 8 | 0.000 1 | 0.560 5 | 0.245 2 | 0.262 3 | 0.713 2 | 0.370 8 | |||||||||||||||||||||||||||||
PPT-SpUNet-F.T. | 0.332 6 | 0.556 3 | 0.270 3 | 0.123 8 | 0.519 2 | 0.091 3 | 0.349 2 | 0.000 1 | 0.000 3 | 0.000 1 | 0.339 6 | 0.383 7 | 0.498 7 | 0.833 4 | 0.807 2 | 0.241 3 | 0.584 3 | 0.000 3 | 0.755 4 | 0.124 4 | 0.000 6 | 0.608 2 | 0.330 4 | 0.530 6 | 0.314 1 | 0.000 5 | 0.374 5 | 0.000 1 | 0.000 3 | 0.197 2 | 0.459 4 | 0.000 6 | 0.000 1 | 0.117 2 | 0.000 2 | 0.876 4 | 0.095 1 | 0.682 4 | 0.000 4 | 0.086 5 | 0.518 4 | 0.433 1 | 0.930 2 | 0.000 1 | 0.000 2 | 0.563 3 | 0.542 8 | 0.077 4 | 0.715 2 | 0.858 5 | 0.756 3 | 0.008 11 | 0.171 7 | 0.874 4 | 0.000 1 | 0.039 3 | 0.550 6 | 0.000 7 | 0.545 4 | 0.256 5 | 0.657 5 | 0.453 2 | 0.351 4 | 0.449 7 | 0.213 3 | 0.392 6 | 0.611 7 | 0.000 4 | 0.037 9 | 0.946 3 | 0.138 8 | 0.000 1 | 0.000 7 | 0.063 5 | 0.308 2 | 0.537 4 | 0.796 2 | 0.673 2 | 0.323 8 | 0.392 6 | 0.400 8 | 0.509 5 | 0.000 3 | 0.000 1 | 0.649 1 | 0.000 6 | 0.023 6 | 0.000 6 | 0.000 3 | 0.914 5 | 0.002 10 | 0.506 10 | 0.163 6 | 0.359 5 | 0.872 4 | 0.000 6 | 0.000 1 | 0.623 4 | 0.112 4 | 0.001 8 | 0.000 4 | 0.000 1 | 0.021 3 | 0.753 1 | 0.565 10 | 0.150 1 | 0.579 2 | 0.806 7 | 0.267 4 | 0.616 1 | 0.042 10 | 0.783 7 | 0.000 3 | 0.374 7 | 0.000 1 | 0.000 4 | 0.000 2 | 0.620 5 | 0.000 1 | 0.000 5 | 0.000 1 | 0.572 9 | 0.634 3 | 0.350 6 | 0.792 3 | 0.000 6 | 0.000 1 | 0.376 5 | 0.535 3 | 0.378 2 | 0.855 3 | 0.672 2 | 0.074 7 | 0.000 7 | 0.185 4 | 0.000 1 | 0.727 6 | 0.660 6 | 0.076 11 | 0.000 7 | 0.432 6 | 0.646 5 | 0.000 1 | 0.594 6 | 0.006 9 | 0.000 5 | 0.000 1 | 0.658 4 | 0.000 2 | 0.000 1 | 0.661 1 | 0.549 5 | 0.300 8 | 0.291 8 | 0.045 8 | 0.942 6 | 0.304 4 | 0.600 5 | 0.572 4 | 0.135 10 | 0.695 2 | 0.000 1 | 0.008 5 | 0.793 4 | 0.942 1 | 0.899 2 | 0.000 1 | 0.816 3 | 0.181 6 | 0.897 2 | 0.000 1 | 0.679 3 | 0.223 3 | 0.264 2 | 0.691 3 | 0.345 9 | |||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
OctFormer ScanNet200 | 0.326 7 | 0.539 6 | 0.265 6 | 0.131 6 | 0.499 4 | 0.110 1 | 0.522 1 | 0.000 1 | 0.000 3 | 0.000 1 | 0.318 8 | 0.427 4 | 0.455 9 | 0.743 9 | 0.765 7 | 0.175 6 | 0.842 1 | 0.000 3 | 0.828 2 | 0.204 1 | 0.033 3 | 0.429 6 | 0.335 2 | 0.601 1 | 0.312 2 | 0.000 5 | 0.357 6 | 0.000 1 | 0.000 3 | 0.047 8 | 0.423 5 | 0.000 6 | 0.000 1 | 0.105 5 | 0.000 2 | 0.873 6 | 0.079 7 | 0.670 7 | 0.000 4 | 0.117 2 | 0.471 8 | 0.432 2 | 0.829 8 | 0.000 1 | 0.000 2 | 0.584 2 | 0.417 11 | 0.089 3 | 0.684 7 | 0.837 6 | 0.705 10 | 0.021 9 | 0.178 6 | 0.892 2 | 0.000 1 | 0.028 4 | 0.505 8 | 0.000 7 | 0.457 6 | 0.200 8 | 0.662 2 | 0.412 6 | 0.244 9 | 0.496 5 | 0.000 11 | 0.451 4 | 0.626 5 | 0.000 4 | 0.102 6 | 0.943 6 | 0.138 8 | 0.000 1 | 0.000 7 | 0.149 4 | 0.291 3 | 0.534 5 | 0.722 3 | 0.632 4 | 0.331 7 | 0.253 10 | 0.453 5 | 0.487 7 | 0.000 3 | 0.000 1 | 0.479 3 | 0.000 6 | 0.022 7 | 0.000 6 | 0.000 3 | 0.900 6 | 0.128 5 | 0.684 2 | 0.164 5 | 0.413 2 | 0.854 8 | 0.000 6 | 0.000 1 | 0.512 11 | 0.074 11 | 0.003 7 | 0.000 4 | 0.000 1 | 0.000 5 | 0.469 9 | 0.613 7 | 0.132 5 | 0.529 4 | 0.871 2 | 0.227 10 | 0.582 4 | 0.026 11 | 0.787 6 | 0.000 3 | 0.339 9 | 0.000 1 | 0.000 4 | 0.000 2 | 0.626 4 | 0.000 1 | 0.029 4 | 0.000 1 | 0.587 5 | 0.612 5 | 0.411 4 | 0.724 7 | 0.000 6 | 0.000 1 | 0.407 3 | 0.552 2 | 0.513 1 | 0.849 4 | 0.655 3 | 0.408 2 | 0.000 7 | 0.296 2 | 0.000 1 | 0.686 9 | 0.645 8 | 0.145 5 | 0.022 5 | 0.414 8 | 0.633 6 | 0.000 1 | 0.637 1 | 0.224 1 | 0.000 5 | 0.000 1 | 0.650 5 | 0.000 2 | 0.000 1 | 0.622 5 | 0.535 7 | 0.343 6 | 0.483 3 | 0.230 7 | 0.943 5 | 0.289 5 | 0.618 4 | 0.596 2 | 0.140 8 | 0.679 4 | 0.000 1 | 0.022 2 | 0.783 6 | 0.620 9 | 0.906 1 | 0.000 1 | 0.806 5 | 0.137 8 | 0.865 3 | 0.000 1 | 0.378 7 | 0.000 9 | 0.168 11 | 0.680 5 | 0.227 10 | |||||||||||||||||||||||||||||
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 | 0.475 7 | 0.057 11 | 0.142 8 | 0.000 1 | 0.000 3 | 0.000 1 | 0.387 3 | 0.463 3 | 0.499 6 | 0.924 2 | 0.774 6 | 0.213 4 | 0.257 7 | 0.000 3 | 0.546 10 | 0.100 7 | 0.006 5 | 0.615 1 | 0.177 11 | 0.534 4 | 0.246 3 | 0.000 5 | 0.400 2 | 0.000 1 | 0.338 1 | 0.006 10 | 0.484 3 | 0.609 2 | 0.000 1 | 0.083 7 | 0.000 2 | 0.873 6 | 0.089 4 | 0.661 8 | 0.000 4 | 0.048 10 | 0.560 1 | 0.408 4 | 0.892 5 | 0.000 1 | 0.000 2 | 0.586 1 | 0.616 5 | 0.000 7 | 0.692 6 | 0.900 2 | 0.721 6 | 0.162 1 | 0.228 3 | 0.860 5 | 0.000 1 | 0.000 7 | 0.575 2 | 0.083 3 | 0.550 3 | 0.347 2 | 0.624 7 | 0.410 7 | 0.360 3 | 0.740 2 | 0.109 8 | 0.321 9 | 0.660 4 | 0.000 4 | 0.121 4 | 0.939 7 | 0.143 6 | 0.000 1 | 0.400 1 | 0.003 7 | 0.190 6 | 0.564 2 | 0.652 6 | 0.615 5 | 0.421 2 | 0.304 9 | 0.579 1 | 0.547 3 | 0.000 3 | 0.000 1 | 0.296 8 | 0.000 6 | 0.030 5 | 0.096 3 | 0.000 3 | 0.916 3 | 0.037 7 | 0.551 6 | 0.171 4 | 0.376 4 | 0.865 5 | 0.286 2 | 0.000 1 | 0.633 2 | 0.102 9 | 0.027 5 | 0.011 3 | 0.000 1 | 0.000 5 | 0.474 8 | 0.742 2 | 0.133 4 | 0.311 7 | 0.824 6 | 0.242 7 | 0.503 8 | 0.068 6 | 0.828 3 | 0.000 3 | 0.429 3 | 0.000 1 | 0.063 3 | 0.000 2 | 0.781 1 | 0.000 1 | 0.000 5 | 0.000 1 | 0.665 1 | 0.633 4 | 0.450 3 | 0.818 2 | 0.000 6 | 0.000 1 | 0.429 2 | 0.532 4 | 0.226 7 | 0.825 5 | 0.510 7 | 0.377 3 | 0.709 1 | 0.079 8 | 0.000 1 | 0.753 2 | 0.683 2 | 0.102 10 | 0.063 3 | 0.401 10 | 0.620 8 | 0.000 1 | 0.619 2 | 0.000 10 | 0.000 5 | 0.000 1 | 0.595 9 | 0.000 2 | 0.000 1 | 0.345 8 | 0.564 3 | 0.411 4 | 0.603 1 | 0.384 3 | 0.945 4 | 0.266 6 | 0.643 3 | 0.367 8 | 0.304 1 | 0.663 6 | 0.000 1 | 0.010 3 | 0.726 9 | 0.767 6 | 0.898 3 | 0.000 1 | 0.784 7 | 0.435 1 | 0.861 5 | 0.000 1 | 0.447 6 | 0.000 9 | 0.257 4 | 0.656 7 | 0.377 7 | |||||||||||||||||||||||||||||
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
AWCS | 0.305 8 | 0.508 8 | 0.225 8 | 0.142 5 | 0.463 8 | 0.063 9 | 0.195 6 | 0.000 1 | 0.000 3 | 0.000 1 | 0.467 2 | 0.551 1 | 0.504 5 | 0.773 5 | 0.764 8 | 0.142 8 | 0.029 11 | 0.000 3 | 0.626 8 | 0.100 7 | 0.000 6 | 0.360 8 | 0.179 9 | 0.507 9 | 0.137 9 | 0.006 4 | 0.300 8 | 0.000 1 | 0.000 3 | 0.172 5 | 0.364 9 | 0.512 4 | 0.000 1 | 0.056 8 | 0.000 2 | 0.865 8 | 0.093 3 | 0.634 11 | 0.000 4 | 0.071 8 | 0.396 9 | 0.296 10 | 0.876 6 | 0.000 1 | 0.000 2 | 0.373 8 | 0.436 10 | 0.063 6 | 0.749 1 | 0.877 4 | 0.721 6 | 0.131 3 | 0.124 8 | 0.804 9 | 0.000 1 | 0.000 7 | 0.515 7 | 0.010 6 | 0.452 7 | 0.252 6 | 0.578 8 | 0.417 5 | 0.179 11 | 0.484 6 | 0.171 4 | 0.337 8 | 0.606 8 | 0.000 4 | 0.115 5 | 0.937 8 | 0.142 7 | 0.000 1 | 0.008 6 | 0.000 9 | 0.157 10 | 0.484 8 | 0.402 11 | 0.501 9 | 0.339 6 | 0.553 3 | 0.529 2 | 0.478 8 | 0.000 3 | 0.000 1 | 0.404 6 | 0.001 5 | 0.022 7 | 0.077 5 | 0.000 3 | 0.894 8 | 0.219 4 | 0.628 4 | 0.093 9 | 0.305 8 | 0.886 1 | 0.233 3 | 0.000 1 | 0.603 6 | 0.112 4 | 0.023 6 | 0.000 4 | 0.000 1 | 0.000 5 | 0.741 2 | 0.664 4 | 0.097 9 | 0.253 8 | 0.782 8 | 0.264 5 | 0.523 7 | 0.154 1 | 0.707 10 | 0.000 3 | 0.411 4 | 0.000 1 | 0.000 4 | 0.000 2 | 0.332 10 | 0.000 1 | 0.000 5 | 0.000 1 | 0.602 3 | 0.595 7 | 0.185 9 | 0.656 10 | 0.159 3 | 0.000 1 | 0.355 7 | 0.424 9 | 0.154 9 | 0.729 9 | 0.516 6 | 0.220 6 | 0.620 2 | 0.084 7 | 0.000 1 | 0.707 8 | 0.651 7 | 0.173 2 | 0.014 6 | 0.381 11 | 0.582 9 | 0.000 1 | 0.619 2 | 0.049 8 | 0.000 5 | 0.000 1 | 0.702 2 | 0.000 2 | 0.000 1 | 0.302 10 | 0.489 9 | 0.317 7 | 0.334 7 | 0.392 2 | 0.922 8 | 0.254 7 | 0.533 8 | 0.394 7 | 0.129 11 | 0.613 9 | 0.000 1 | 0.000 8 | 0.820 2 | 0.649 8 | 0.749 8 | 0.000 1 | 0.782 8 | 0.282 5 | 0.863 4 | 0.000 1 | 0.288 10 | 0.006 6 | 0.220 7 | 0.633 8 | 0.542 2 | |||||||||||||||||||||||||||||
LGround | 0.272 9 | 0.485 9 | 0.184 9 | 0.106 9 | 0.476 6 | 0.077 6 | 0.218 5 | 0.000 1 | 0.000 3 | 0.000 1 | 0.547 1 | 0.295 8 | 0.540 3 | 0.746 8 | 0.745 9 | 0.058 10 | 0.112 10 | 0.005 1 | 0.658 6 | 0.077 11 | 0.000 6 | 0.322 9 | 0.178 10 | 0.512 8 | 0.190 7 | 0.199 1 | 0.277 9 | 0.000 1 | 0.000 3 | 0.173 4 | 0.399 6 | 0.000 6 | 0.000 1 | 0.039 10 | 0.000 2 | 0.858 9 | 0.085 5 | 0.676 6 | 0.002 2 | 0.103 3 | 0.498 5 | 0.323 8 | 0.703 9 | 0.000 1 | 0.000 2 | 0.296 9 | 0.549 7 | 0.216 1 | 0.702 4 | 0.768 8 | 0.718 8 | 0.028 7 | 0.092 10 | 0.786 10 | 0.000 1 | 0.000 7 | 0.453 10 | 0.022 5 | 0.251 11 | 0.252 6 | 0.572 9 | 0.348 9 | 0.321 5 | 0.514 4 | 0.063 9 | 0.279 10 | 0.552 9 | 0.000 4 | 0.019 10 | 0.932 9 | 0.132 10 | 0.000 1 | 0.000 7 | 0.000 9 | 0.156 11 | 0.457 9 | 0.623 7 | 0.518 8 | 0.265 10 | 0.358 7 | 0.381 9 | 0.395 9 | 0.000 3 | 0.000 1 | 0.127 11 | 0.012 3 | 0.051 1 | 0.000 6 | 0.000 3 | 0.886 9 | 0.014 8 | 0.437 11 | 0.179 3 | 0.244 9 | 0.826 9 | 0.000 6 | 0.000 1 | 0.599 7 | 0.136 1 | 0.085 3 | 0.000 4 | 0.000 1 | 0.000 5 | 0.565 7 | 0.612 8 | 0.143 2 | 0.207 9 | 0.566 9 | 0.232 9 | 0.446 9 | 0.127 2 | 0.708 9 | 0.000 3 | 0.384 5 | 0.000 1 | 0.000 4 | 0.000 2 | 0.402 8 | 0.000 1 | 0.059 3 | 0.000 1 | 0.525 11 | 0.566 8 | 0.229 8 | 0.659 9 | 0.000 6 | 0.000 1 | 0.265 9 | 0.446 8 | 0.147 10 | 0.720 11 | 0.597 5 | 0.066 8 | 0.000 7 | 0.187 3 | 0.000 1 | 0.726 7 | 0.467 11 | 0.134 7 | 0.000 7 | 0.413 9 | 0.629 7 | 0.000 1 | 0.363 10 | 0.055 7 | 0.022 2 | 0.000 1 | 0.626 7 | 0.000 2 | 0.000 1 | 0.323 9 | 0.479 11 | 0.154 10 | 0.117 9 | 0.028 10 | 0.901 9 | 0.243 9 | 0.415 10 | 0.295 11 | 0.143 6 | 0.610 10 | 0.000 1 | 0.000 8 | 0.777 7 | 0.397 11 | 0.324 10 | 0.000 1 | 0.778 9 | 0.179 7 | 0.702 10 | 0.000 1 | 0.274 11 | 0.404 1 | 0.233 6 | 0.622 9 | 0.398 5 | |||||||||||||||||||||||||||||
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CSC-Pretrain | 0.249 11 | 0.455 11 | 0.171 10 | 0.079 11 | 0.418 10 | 0.059 10 | 0.186 7 | 0.000 1 | 0.000 3 | 0.000 1 | 0.335 7 | 0.250 9 | 0.316 10 | 0.766 6 | 0.697 11 | 0.142 8 | 0.170 8 | 0.003 2 | 0.553 9 | 0.112 5 | 0.097 1 | 0.201 11 | 0.186 8 | 0.476 11 | 0.081 10 | 0.000 5 | 0.216 11 | 0.000 1 | 0.000 3 | 0.001 11 | 0.314 11 | 0.000 6 | 0.000 1 | 0.055 9 | 0.000 2 | 0.832 11 | 0.094 2 | 0.659 9 | 0.002 2 | 0.076 6 | 0.310 11 | 0.293 11 | 0.664 11 | 0.000 1 | 0.000 2 | 0.175 11 | 0.634 4 | 0.130 2 | 0.552 11 | 0.686 11 | 0.700 11 | 0.076 5 | 0.110 9 | 0.770 11 | 0.000 1 | 0.000 7 | 0.430 11 | 0.000 7 | 0.319 9 | 0.166 9 | 0.542 11 | 0.327 10 | 0.205 10 | 0.332 10 | 0.052 10 | 0.375 7 | 0.444 11 | 0.000 4 | 0.012 11 | 0.930 11 | 0.203 1 | 0.000 1 | 0.000 7 | 0.046 6 | 0.175 8 | 0.413 10 | 0.592 8 | 0.471 10 | 0.299 9 | 0.152 11 | 0.340 10 | 0.247 11 | 0.000 3 | 0.000 1 | 0.225 9 | 0.058 2 | 0.037 2 | 0.000 6 | 0.207 1 | 0.862 10 | 0.014 8 | 0.548 7 | 0.033 10 | 0.233 10 | 0.816 10 | 0.000 6 | 0.000 1 | 0.542 10 | 0.123 3 | 0.121 1 | 0.019 2 | 0.000 1 | 0.000 5 | 0.463 10 | 0.454 11 | 0.045 11 | 0.128 11 | 0.557 10 | 0.235 8 | 0.441 10 | 0.063 8 | 0.484 11 | 0.000 3 | 0.308 11 | 0.000 1 | 0.000 4 | 0.000 2 | 0.318 11 | 0.000 1 | 0.000 5 | 0.000 1 | 0.545 10 | 0.543 9 | 0.164 10 | 0.734 6 | 0.000 6 | 0.000 1 | 0.215 11 | 0.371 10 | 0.198 8 | 0.743 8 | 0.205 10 | 0.062 9 | 0.000 7 | 0.079 8 | 0.000 1 | 0.683 10 | 0.547 10 | 0.142 6 | 0.000 7 | 0.441 5 | 0.579 10 | 0.000 1 | 0.464 9 | 0.098 6 | 0.041 1 | 0.000 1 | 0.590 10 | 0.000 2 | 0.000 1 | 0.373 7 | 0.494 8 | 0.174 9 | 0.105 10 | 0.001 11 | 0.895 10 | 0.222 10 | 0.537 7 | 0.307 10 | 0.180 5 | 0.625 8 | 0.000 1 | 0.000 8 | 0.591 11 | 0.609 10 | 0.398 9 | 0.000 1 | 0.766 11 | 0.014 11 | 0.638 11 | 0.000 1 | 0.377 8 | 0.004 7 | 0.206 9 | 0.609 11 | 0.465 3 | |||||||||||||||||||||||||||||
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 | 0.381 11 | 0.084 4 | 0.134 9 | 0.000 1 | 0.000 3 | 0.000 1 | 0.386 4 | 0.141 11 | 0.279 11 | 0.737 10 | 0.703 10 | 0.014 11 | 0.164 9 | 0.000 3 | 0.663 5 | 0.092 10 | 0.000 6 | 0.224 10 | 0.291 5 | 0.531 5 | 0.056 11 | 0.000 5 | 0.242 10 | 0.000 1 | 0.000 3 | 0.013 9 | 0.331 10 | 0.000 6 | 0.000 1 | 0.035 11 | 0.001 1 | 0.858 9 | 0.059 10 | 0.650 10 | 0.000 4 | 0.056 9 | 0.353 10 | 0.299 9 | 0.670 10 | 0.000 1 | 0.000 2 | 0.284 10 | 0.484 9 | 0.071 5 | 0.594 10 | 0.720 10 | 0.710 9 | 0.027 8 | 0.068 11 | 0.813 8 | 0.000 1 | 0.005 6 | 0.492 9 | 0.164 1 | 0.274 10 | 0.111 10 | 0.571 10 | 0.307 11 | 0.293 7 | 0.307 11 | 0.150 5 | 0.163 11 | 0.531 10 | 0.002 3 | 0.545 3 | 0.932 9 | 0.093 11 | 0.000 1 | 0.000 7 | 0.002 8 | 0.159 9 | 0.368 11 | 0.581 9 | 0.440 11 | 0.228 11 | 0.406 5 | 0.282 11 | 0.294 10 | 0.000 3 | 0.000 1 | 0.189 10 | 0.060 1 | 0.036 3 | 0.000 6 | 0.000 3 | 0.897 7 | 0.000 11 | 0.525 8 | 0.025 11 | 0.205 11 | 0.771 11 | 0.000 6 | 0.000 1 | 0.593 8 | 0.108 7 | 0.044 4 | 0.000 4 | 0.000 1 | 0.000 5 | 0.282 11 | 0.589 9 | 0.094 10 | 0.169 10 | 0.466 11 | 0.227 10 | 0.419 11 | 0.125 3 | 0.757 8 | 0.002 1 | 0.334 10 | 0.000 1 | 0.000 4 | 0.000 2 | 0.357 9 | 0.000 1 | 0.000 5 | 0.000 1 | 0.582 6 | 0.513 11 | 0.337 7 | 0.612 11 | 0.000 6 | 0.000 1 | 0.250 10 | 0.352 11 | 0.136 11 | 0.724 10 | 0.655 3 | 0.280 5 | 0.000 7 | 0.046 10 | 0.000 1 | 0.606 11 | 0.559 9 | 0.159 4 | 0.102 1 | 0.445 4 | 0.655 4 | 0.000 1 | 0.310 11 | 0.117 3 | 0.000 5 | 0.000 1 | 0.581 11 | 0.026 1 | 0.000 1 | 0.265 11 | 0.483 10 | 0.084 11 | 0.097 11 | 0.044 9 | 0.865 11 | 0.142 11 | 0.588 6 | 0.351 9 | 0.272 2 | 0.596 11 | 0.000 1 | 0.003 6 | 0.622 10 | 0.720 7 | 0.096 11 | 0.000 1 | 0.771 10 | 0.016 10 | 0.772 9 | 0.000 1 | 0.302 9 | 0.194 4 | 0.214 8 | 0.621 10 | 0.197 11 | |||||||||||||||||||||||||||||
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 |