ScanNet200 3D Semantic Label Benchmark
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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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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 |