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