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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DITR | 0.409 2 | 0.616 1 | 0.351 1 | 0.215 3 | 0.831 3 | 0.791 1 | 0.947 6 | 0.619 1 | 0.730 2 | 0.762 3 | 0.494 2 | 0.571 1 | 0.597 2 | 0.000 11 | 0.853 1 | 0.625 3 | 0.796 2 | 0.301 3 | 0.723 3 | 0.959 4 | 0.617 2 | 0.862 7 | 0.917 3 | 0.573 1 | 0.562 10 | 0.591 1 | 0.784 7 | 0.504 5 | 0.757 1 | 0.737 2 | 0.429 4 | 0.853 1 | 0.662 3 | 0.135 3 | 0.459 3 | 0.558 10 | 0.000 1 | 0.913 1 | 0.878 7 | 0.687 14 | 0.008 15 | 0.000 7 | 0.615 4 | 0.238 1 | 0.651 1 | 0.370 3 | 0.742 2 | 0.925 2 | 0.360 1 | 0.167 6 | 0.938 1 | 0.752 2 | 0.118 3 | 0.827 2 | 0.670 4 | 0.723 2 | 0.614 3 | 0.628 14 | 0.372 1 | 0.000 3 | 0.143 12 | 0.175 16 | 0.873 3 | 0.652 1 | 0.991 1 | 0.340 1 | 0.148 6 | 0.814 1 | 0.656 1 | 0.524 6 | 0.491 3 | 0.000 1 | 0.743 1 | 0.752 2 | 0.000 4 | 0.000 9 | 0.000 1 | 0.399 1 | 0.865 3 | 0.953 1 | 0.833 1 | 0.694 2 | 0.444 6 | 0.000 15 | 0.688 4 | 0.609 2 | 0.000 3 | 0.053 2 | 0.000 1 | 0.022 10 | 0.000 4 | 0.053 13 | 0.940 3 | 0.000 1 | 0.186 1 | 0.093 5 | 0.854 3 | 0.877 1 | 0.534 2 | 0.404 1 | 0.270 3 | 0.191 10 | 0.198 4 | 0.461 8 | 0.375 1 | 0.152 3 | 0.921 1 | 0.132 9 | 0.235 14 | 0.000 1 | 0.617 1 | 0.330 1 | 0.896 1 | 0.399 6 | 0.431 5 | 0.597 10 | 0.759 1 | 0.554 4 | 0.400 2 | 0.000 1 | 0.559 9 | 0.699 1 | 0.852 2 | 0.000 1 | 0.000 8 | 0.091 10 | 0.385 1 | 0.000 6 | 0.000 9 | 0.478 4 | 0.077 9 | 0.000 7 | 0.140 4 | 0.000 2 | 0.000 3 | 0.670 13 | 0.000 1 | 0.452 5 | 0.000 1 | 0.263 3 | 0.361 13 | 0.000 1 | 0.643 4 | 0.000 1 | 0.000 1 | 0.357 5 | 0.005 10 | 0.928 2 | 0.362 1 | 0.496 1 | 0.000 1 | 0.000 6 | 0.000 1 | 0.072 16 | 0.585 2 | 0.587 3 | 0.476 4 | 0.037 15 | 0.191 5 | 0.410 13 | 0.629 6 | 0.118 1 | 0.000 1 | 0.479 11 | 0.000 2 | 0.000 2 | 0.107 8 | 0.839 3 | 0.000 2 | 0.000 1 | 0.000 1 | 0.139 11 | 0.036 5 | 0.000 4 | 0.247 9 | 0.000 1 | |||||||||||||||||||||||||||||
PTv3 ScanNet200 | 0.393 3 | 0.592 3 | 0.330 2 | 0.216 2 | 0.851 2 | 0.687 8 | 0.971 2 | 0.586 2 | 0.755 1 | 0.752 9 | 0.505 1 | 0.404 7 | 0.575 4 | 0.000 11 | 0.848 2 | 0.616 4 | 0.761 3 | 0.349 1 | 0.738 2 | 0.978 2 | 0.546 6 | 0.860 8 | 0.926 2 | 0.346 4 | 0.654 4 | 0.384 6 | 0.828 1 | 0.523 3 | 0.699 3 | 0.583 6 | 0.387 8 | 0.822 5 | 0.688 2 | 0.118 5 | 0.474 2 | 0.603 5 | 0.000 1 | 0.832 5 | 0.903 2 | 0.753 9 | 0.140 9 | 0.000 7 | 0.650 3 | 0.109 4 | 0.520 4 | 0.457 1 | 0.497 10 | 0.871 4 | 0.281 4 | 0.192 4 | 0.887 4 | 0.748 3 | 0.168 1 | 0.727 7 | 0.733 2 | 0.740 1 | 0.644 1 | 0.714 5 | 0.190 11 | 0.000 3 | 0.256 5 | 0.449 7 | 0.914 1 | 0.514 3 | 0.759 14 | 0.337 2 | 0.172 4 | 0.692 6 | 0.617 2 | 0.636 1 | 0.325 7 | 0.000 1 | 0.641 2 | 0.782 1 | 0.000 4 | 0.065 4 | 0.000 1 | 0.000 6 | 0.842 5 | 0.903 2 | 0.661 4 | 0.662 4 | 0.612 1 | 0.405 2 | 0.731 1 | 0.566 3 | 0.000 3 | 0.000 7 | 0.000 1 | 0.017 14 | 0.301 1 | 0.088 6 | 0.941 2 | 0.000 1 | 0.077 3 | 0.000 10 | 0.717 7 | 0.790 2 | 0.310 12 | 0.026 16 | 0.264 4 | 0.349 1 | 0.220 3 | 0.397 12 | 0.366 2 | 0.115 11 | 0.000 5 | 0.337 2 | 0.463 6 | 0.000 1 | 0.531 3 | 0.218 3 | 0.593 2 | 0.455 2 | 0.469 1 | 0.708 4 | 0.210 4 | 0.592 3 | 0.108 15 | 0.000 1 | 0.728 1 | 0.682 3 | 0.671 8 | 0.000 1 | 0.000 8 | 0.407 1 | 0.136 3 | 0.022 3 | 0.575 1 | 0.436 5 | 0.259 3 | 0.428 1 | 0.048 5 | 0.000 2 | 0.000 3 | 0.879 5 | 0.000 1 | 0.480 3 | 0.000 1 | 0.133 8 | 0.597 1 | 0.000 1 | 0.690 2 | 0.000 1 | 0.000 1 | 0.009 14 | 0.000 14 | 0.921 4 | 0.000 8 | 0.151 4 | 0.000 1 | 0.000 6 | 0.000 1 | 0.109 8 | 0.494 12 | 0.622 2 | 0.394 8 | 0.073 11 | 0.141 8 | 0.798 1 | 0.528 7 | 0.026 5 | 0.000 1 | 0.551 5 | 0.000 2 | 0.000 2 | 0.134 6 | 0.717 8 | 0.000 2 | 0.000 1 | 0.000 1 | 0.188 3 | 0.000 8 | 0.000 4 | 0.791 2 | 0.000 1 | |||||||||||||||||||||||||||||
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
OA-CNN-L_ScanNet200 | 0.333 10 | 0.558 4 | 0.269 9 | 0.124 12 | 0.821 5 | 0.703 4 | 0.946 8 | 0.569 4 | 0.662 6 | 0.748 10 | 0.487 3 | 0.455 3 | 0.572 6 | 0.000 11 | 0.789 8 | 0.534 10 | 0.736 10 | 0.271 6 | 0.713 4 | 0.949 7 | 0.498 14 | 0.877 3 | 0.860 9 | 0.332 7 | 0.706 1 | 0.474 2 | 0.788 6 | 0.406 12 | 0.637 6 | 0.495 10 | 0.355 11 | 0.805 7 | 0.592 13 | 0.015 14 | 0.396 5 | 0.602 6 | 0.000 1 | 0.799 9 | 0.876 8 | 0.713 13 | 0.276 4 | 0.000 7 | 0.493 11 | 0.080 8 | 0.448 14 | 0.363 5 | 0.661 4 | 0.833 6 | 0.262 6 | 0.125 8 | 0.823 10 | 0.665 9 | 0.076 10 | 0.720 8 | 0.557 9 | 0.637 9 | 0.517 9 | 0.672 11 | 0.227 7 | 0.000 3 | 0.158 11 | 0.496 5 | 0.843 11 | 0.352 9 | 0.835 12 | 0.000 4 | 0.103 12 | 0.711 5 | 0.527 3 | 0.526 5 | 0.320 8 | 0.000 1 | 0.568 6 | 0.625 11 | 0.067 1 | 0.000 9 | 0.000 1 | 0.001 5 | 0.806 7 | 0.836 8 | 0.621 9 | 0.591 7 | 0.373 8 | 0.314 5 | 0.668 8 | 0.398 9 | 0.003 2 | 0.000 7 | 0.000 1 | 0.016 15 | 0.024 3 | 0.043 14 | 0.906 6 | 0.000 1 | 0.052 5 | 0.000 10 | 0.384 11 | 0.330 12 | 0.342 7 | 0.100 10 | 0.223 7 | 0.183 12 | 0.112 6 | 0.476 5 | 0.313 7 | 0.130 10 | 0.196 3 | 0.112 11 | 0.370 12 | 0.000 1 | 0.234 10 | 0.071 9 | 0.160 8 | 0.403 5 | 0.398 11 | 0.492 14 | 0.197 5 | 0.076 11 | 0.272 5 | 0.000 1 | 0.200 16 | 0.560 8 | 0.735 6 | 0.000 1 | 0.000 8 | 0.000 11 | 0.110 8 | 0.002 5 | 0.021 8 | 0.412 6 | 0.000 11 | 0.000 7 | 0.000 11 | 0.000 2 | 0.000 3 | 0.794 10 | 0.000 1 | 0.445 6 | 0.000 1 | 0.022 10 | 0.509 6 | 0.000 1 | 0.517 14 | 0.000 1 | 0.000 1 | 0.001 15 | 0.245 3 | 0.915 6 | 0.024 4 | 0.089 5 | 0.000 1 | 0.262 2 | 0.000 1 | 0.103 11 | 0.524 8 | 0.392 11 | 0.515 2 | 0.013 16 | 0.251 4 | 0.411 12 | 0.662 3 | 0.001 11 | 0.000 1 | 0.473 12 | 0.000 2 | 0.000 2 | 0.150 5 | 0.699 9 | 0.000 2 | 0.000 1 | 0.000 1 | 0.166 6 | 0.000 8 | 0.024 3 | 0.000 10 | 0.000 1 | |||||||||||||||||||||||||||||
L3DETR-ScanNet_200 | 0.336 9 | 0.533 12 | 0.279 5 | 0.155 9 | 0.801 11 | 0.689 6 | 0.946 8 | 0.539 9 | 0.660 9 | 0.759 5 | 0.380 13 | 0.333 12 | 0.583 3 | 0.000 11 | 0.788 9 | 0.529 11 | 0.740 9 | 0.261 11 | 0.679 9 | 0.940 11 | 0.525 11 | 0.860 8 | 0.883 7 | 0.226 12 | 0.613 8 | 0.397 5 | 0.720 10 | 0.512 4 | 0.565 11 | 0.620 3 | 0.417 6 | 0.775 12 | 0.629 7 | 0.158 2 | 0.298 11 | 0.579 9 | 0.000 1 | 0.835 3 | 0.883 6 | 0.927 1 | 0.114 10 | 0.079 4 | 0.511 10 | 0.073 10 | 0.508 6 | 0.312 6 | 0.629 5 | 0.861 5 | 0.192 14 | 0.098 14 | 0.908 3 | 0.636 12 | 0.032 16 | 0.563 16 | 0.514 13 | 0.664 5 | 0.505 10 | 0.697 7 | 0.225 8 | 0.000 3 | 0.264 3 | 0.411 9 | 0.860 8 | 0.321 12 | 0.960 2 | 0.058 3 | 0.109 11 | 0.776 3 | 0.526 4 | 0.557 2 | 0.303 9 | 0.000 1 | 0.339 11 | 0.712 5 | 0.000 4 | 0.014 7 | 0.000 1 | 0.000 6 | 0.638 11 | 0.856 5 | 0.641 7 | 0.579 11 | 0.107 16 | 0.119 12 | 0.661 9 | 0.416 7 | 0.000 3 | 0.000 7 | 0.000 1 | 0.007 16 | 0.000 4 | 0.067 9 | 0.910 5 | 0.000 1 | 0.000 10 | 0.000 10 | 0.463 10 | 0.448 8 | 0.294 14 | 0.324 2 | 0.293 2 | 0.211 7 | 0.108 8 | 0.448 9 | 0.068 16 | 0.141 7 | 0.000 5 | 0.330 3 | 0.699 1 | 0.000 1 | 0.256 9 | 0.192 5 | 0.000 14 | 0.355 8 | 0.418 7 | 0.209 16 | 0.146 11 | 0.679 1 | 0.101 16 | 0.000 1 | 0.503 14 | 0.687 2 | 0.671 8 | 0.000 1 | 0.000 8 | 0.174 9 | 0.117 6 | 0.000 6 | 0.122 6 | 0.515 2 | 0.104 6 | 0.259 2 | 0.312 3 | 0.000 2 | 0.000 3 | 0.765 11 | 0.000 1 | 0.369 13 | 0.000 1 | 0.183 6 | 0.422 11 | 0.000 1 | 0.646 3 | 0.000 1 | 0.000 1 | 0.565 2 | 0.001 13 | 0.125 16 | 0.010 5 | 0.002 7 | 0.000 1 | 0.487 1 | 0.000 1 | 0.075 14 | 0.548 4 | 0.420 9 | 0.233 14 | 0.082 8 | 0.138 12 | 0.430 11 | 0.427 12 | 0.000 12 | 0.000 1 | 0.549 6 | 0.000 2 | 0.000 2 | 0.074 9 | 0.409 15 | 0.000 2 | 0.000 1 | 0.000 1 | 0.152 7 | 0.051 4 | 0.000 4 | 0.598 4 | 0.000 1 | |||||||||||||||||||||||||||||
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
GSTran | 0.339 7 | 0.536 10 | 0.273 6 | 0.169 7 | 0.811 7 | 0.690 5 | 0.949 5 | 0.506 12 | 0.690 4 | 0.765 2 | 0.397 12 | 0.235 15 | 0.480 13 | 0.014 8 | 0.788 9 | 0.593 7 | 0.746 8 | 0.282 5 | 0.696 5 | 0.913 13 | 0.538 7 | 0.853 11 | 0.889 6 | 0.286 11 | 0.670 3 | 0.310 12 | 0.682 13 | 0.445 9 | 0.638 5 | 0.598 4 | 0.358 10 | 0.841 2 | 0.643 4 | 0.061 8 | 0.373 7 | 0.614 4 | 0.000 1 | 0.786 11 | 0.876 8 | 0.754 8 | 0.357 1 | 0.000 7 | 0.535 8 | 0.071 12 | 0.491 9 | 0.369 4 | 0.487 11 | 0.698 11 | 0.317 3 | 0.202 3 | 0.659 16 | 0.666 8 | 0.086 8 | 0.832 1 | 0.461 15 | 0.597 13 | 0.455 12 | 0.731 3 | 0.156 13 | 0.000 3 | 0.316 1 | 0.318 13 | 0.784 13 | 0.348 10 | 0.896 7 | 0.000 4 | 0.084 14 | 0.648 8 | 0.514 5 | 0.470 11 | 0.368 6 | 0.000 1 | 0.441 8 | 0.705 6 | 0.000 4 | 0.079 3 | 0.000 1 | 0.021 3 | 0.872 2 | 0.872 3 | 0.621 9 | 0.589 8 | 0.144 15 | 0.129 11 | 0.648 12 | 0.459 6 | 0.000 3 | 0.000 7 | 0.000 1 | 0.022 10 | 0.289 2 | 0.096 5 | 0.667 13 | 0.000 1 | 0.000 10 | 0.000 10 | 0.834 5 | 0.682 5 | 0.178 16 | 0.033 15 | 0.256 5 | 0.196 8 | 0.000 14 | 0.473 6 | 0.279 8 | 0.079 15 | 0.008 4 | 0.495 1 | 0.425 8 | 0.000 1 | 0.228 12 | 0.009 14 | 0.564 3 | 0.410 4 | 0.366 13 | 0.665 6 | 0.161 10 | 0.615 2 | 0.365 3 | 0.000 1 | 0.609 6 | 0.386 10 | 0.681 7 | 0.000 1 | 0.000 8 | 0.199 7 | 0.093 11 | 0.497 1 | 0.109 7 | 0.252 11 | 0.161 4 | 0.118 5 | 0.000 11 | 0.000 2 | 0.000 3 | 0.857 6 | 0.000 1 | 0.495 1 | 0.000 1 | 0.162 7 | 0.412 12 | 0.000 1 | 0.563 12 | 0.000 1 | 0.000 1 | 0.000 16 | 0.012 7 | 0.877 13 | 0.004 7 | 0.000 8 | 0.000 1 | 0.002 5 | 0.000 1 | 0.109 8 | 0.458 13 | 0.358 12 | 0.246 13 | 0.060 13 | 0.139 10 | 0.466 9 | 0.803 1 | 0.097 2 | 0.000 1 | 0.517 8 | 0.000 2 | 0.000 2 | 0.060 10 | 0.413 14 | 0.000 2 | 0.000 1 | 0.000 1 | 0.183 4 | 0.024 6 | 0.000 4 | 0.297 7 | 0.000 1 | |||||||||||||||||||||||||||||
BFANet ScanNet200 | 0.360 4 | 0.553 6 | 0.293 4 | 0.193 4 | 0.827 4 | 0.689 6 | 0.970 3 | 0.528 11 | 0.661 8 | 0.753 8 | 0.436 7 | 0.378 8 | 0.469 14 | 0.042 5 | 0.810 3 | 0.654 1 | 0.760 4 | 0.266 8 | 0.659 10 | 0.973 3 | 0.574 4 | 0.849 12 | 0.897 4 | 0.382 2 | 0.546 12 | 0.372 8 | 0.698 12 | 0.491 6 | 0.617 9 | 0.526 8 | 0.436 1 | 0.764 13 | 0.476 16 | 0.101 6 | 0.409 4 | 0.585 8 | 0.000 1 | 0.835 3 | 0.901 3 | 0.810 5 | 0.102 12 | 0.000 7 | 0.688 2 | 0.096 5 | 0.483 10 | 0.264 11 | 0.612 8 | 0.591 15 | 0.358 2 | 0.161 7 | 0.863 5 | 0.707 4 | 0.128 2 | 0.814 3 | 0.669 5 | 0.629 10 | 0.563 4 | 0.651 13 | 0.258 4 | 0.000 3 | 0.194 9 | 0.494 6 | 0.806 12 | 0.394 6 | 0.953 4 | 0.000 4 | 0.233 1 | 0.757 4 | 0.508 6 | 0.556 3 | 0.476 4 | 0.000 1 | 0.573 5 | 0.741 4 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.000 16 | 0.852 6 | 0.678 3 | 0.616 5 | 0.460 4 | 0.338 3 | 0.710 2 | 0.534 4 | 0.000 3 | 0.025 4 | 0.000 1 | 0.043 2 | 0.000 4 | 0.056 11 | 0.493 15 | 0.000 1 | 0.000 10 | 0.109 4 | 0.785 6 | 0.590 6 | 0.298 13 | 0.282 4 | 0.143 12 | 0.262 5 | 0.053 11 | 0.526 4 | 0.337 5 | 0.215 1 | 0.000 5 | 0.135 8 | 0.510 4 | 0.000 1 | 0.596 2 | 0.043 12 | 0.511 5 | 0.321 12 | 0.459 2 | 0.772 2 | 0.124 12 | 0.060 13 | 0.266 6 | 0.000 1 | 0.574 8 | 0.568 7 | 0.653 10 | 0.000 1 | 0.093 1 | 0.298 2 | 0.239 2 | 0.000 6 | 0.516 2 | 0.129 13 | 0.284 2 | 0.000 7 | 0.431 1 | 0.000 2 | 0.000 3 | 0.848 7 | 0.000 1 | 0.492 2 | 0.000 1 | 0.376 2 | 0.522 5 | 0.000 1 | 0.469 16 | 0.000 1 | 0.000 1 | 0.330 6 | 0.151 6 | 0.875 14 | 0.000 8 | 0.254 3 | 0.000 1 | 0.000 6 | 0.000 1 | 0.088 13 | 0.661 1 | 0.481 5 | 0.255 11 | 0.105 1 | 0.139 10 | 0.666 4 | 0.641 4 | 0.000 12 | 0.000 1 | 0.614 2 | 0.000 2 | 0.000 2 | 0.000 11 | 0.921 1 | 0.000 2 | 0.000 1 | 0.000 1 | 0.497 1 | 0.000 8 | 0.000 4 | 0.000 10 | 0.000 1 | |||||||||||||||||||||||||||||
ALS-MinkowskiNet | 0.414 1 | 0.610 2 | 0.322 3 | 0.271 1 | 0.852 1 | 0.710 3 | 0.973 1 | 0.572 3 | 0.719 3 | 0.795 1 | 0.477 5 | 0.506 2 | 0.601 1 | 0.000 11 | 0.804 5 | 0.646 2 | 0.804 1 | 0.344 2 | 0.777 1 | 0.984 1 | 0.671 1 | 0.879 2 | 0.936 1 | 0.342 5 | 0.632 6 | 0.449 3 | 0.817 3 | 0.475 7 | 0.723 2 | 0.798 1 | 0.376 9 | 0.832 3 | 0.693 1 | 0.031 10 | 0.564 1 | 0.510 12 | 0.000 1 | 0.893 2 | 0.905 1 | 0.672 16 | 0.314 2 | 0.000 7 | 0.718 1 | 0.153 2 | 0.542 2 | 0.397 2 | 0.726 3 | 0.752 9 | 0.252 8 | 0.226 1 | 0.916 2 | 0.800 1 | 0.047 15 | 0.807 4 | 0.769 1 | 0.709 3 | 0.630 2 | 0.769 1 | 0.217 9 | 0.000 3 | 0.285 2 | 0.598 3 | 0.846 10 | 0.535 2 | 0.956 3 | 0.000 4 | 0.137 9 | 0.784 2 | 0.464 7 | 0.463 12 | 0.230 11 | 0.000 1 | 0.598 3 | 0.662 8 | 0.000 4 | 0.087 2 | 0.000 1 | 0.135 2 | 0.900 1 | 0.780 13 | 0.703 2 | 0.741 1 | 0.571 2 | 0.149 9 | 0.697 3 | 0.646 1 | 0.000 3 | 0.076 1 | 0.000 1 | 0.025 8 | 0.000 4 | 0.106 4 | 0.981 1 | 0.000 1 | 0.043 6 | 0.113 3 | 0.888 1 | 0.248 15 | 0.404 4 | 0.252 6 | 0.314 1 | 0.220 6 | 0.245 1 | 0.466 7 | 0.366 2 | 0.159 2 | 0.000 5 | 0.149 7 | 0.690 2 | 0.000 1 | 0.531 3 | 0.253 2 | 0.285 7 | 0.460 1 | 0.440 4 | 0.813 1 | 0.230 2 | 0.283 6 | 0.159 11 | 0.000 1 | 0.728 1 | 0.666 5 | 0.958 1 | 0.000 1 | 0.021 4 | 0.252 4 | 0.118 4 | 0.000 6 | 0.445 3 | 0.223 12 | 0.285 1 | 0.194 3 | 0.390 2 | 0.000 2 | 0.475 2 | 0.842 8 | 0.000 1 | 0.455 4 | 0.000 1 | 0.250 5 | 0.458 8 | 0.000 1 | 0.865 1 | 0.000 1 | 0.000 1 | 0.635 1 | 0.359 2 | 0.972 1 | 0.087 3 | 0.447 2 | 0.000 1 | 0.000 6 | 0.000 1 | 0.129 2 | 0.532 7 | 0.446 8 | 0.503 3 | 0.071 12 | 0.135 13 | 0.699 3 | 0.717 2 | 0.097 2 | 0.000 1 | 0.665 1 | 0.000 2 | 0.000 2 | 1.000 1 | 0.752 6 | 0.000 2 | 0.000 1 | 0.000 1 | 0.142 9 | 0.200 1 | 0.259 1 | 1.000 1 | 0.000 1 | |||||||||||||||||||||||||||||
OctFormer ScanNet200 | 0.326 12 | 0.539 9 | 0.265 11 | 0.131 11 | 0.806 10 | 0.670 12 | 0.943 11 | 0.535 10 | 0.662 6 | 0.705 15 | 0.423 8 | 0.407 5 | 0.505 10 | 0.003 9 | 0.765 12 | 0.582 8 | 0.686 14 | 0.227 15 | 0.680 8 | 0.943 9 | 0.601 3 | 0.854 10 | 0.892 5 | 0.335 6 | 0.417 16 | 0.357 9 | 0.724 9 | 0.453 8 | 0.632 8 | 0.596 5 | 0.432 3 | 0.783 10 | 0.512 15 | 0.021 13 | 0.244 14 | 0.637 1 | 0.000 1 | 0.787 10 | 0.873 11 | 0.743 11 | 0.000 16 | 0.000 7 | 0.534 9 | 0.110 3 | 0.499 7 | 0.289 9 | 0.626 6 | 0.620 13 | 0.168 16 | 0.204 2 | 0.849 7 | 0.679 7 | 0.117 4 | 0.633 11 | 0.684 3 | 0.650 7 | 0.552 5 | 0.684 10 | 0.312 3 | 0.000 3 | 0.175 10 | 0.429 8 | 0.865 5 | 0.413 4 | 0.837 11 | 0.000 4 | 0.145 7 | 0.626 9 | 0.451 8 | 0.487 9 | 0.513 2 | 0.000 1 | 0.529 7 | 0.613 12 | 0.000 4 | 0.033 6 | 0.000 1 | 0.000 6 | 0.828 6 | 0.871 4 | 0.622 8 | 0.587 9 | 0.411 7 | 0.137 10 | 0.645 13 | 0.343 11 | 0.000 3 | 0.000 7 | 0.000 1 | 0.022 10 | 0.000 4 | 0.026 16 | 0.829 10 | 0.000 1 | 0.022 8 | 0.089 6 | 0.842 4 | 0.253 14 | 0.318 11 | 0.296 3 | 0.178 9 | 0.291 3 | 0.224 2 | 0.584 2 | 0.200 13 | 0.132 9 | 0.000 5 | 0.128 10 | 0.227 15 | 0.000 1 | 0.230 11 | 0.047 11 | 0.149 9 | 0.331 10 | 0.412 9 | 0.618 8 | 0.164 8 | 0.102 9 | 0.522 1 | 0.000 1 | 0.655 4 | 0.378 11 | 0.469 14 | 0.000 1 | 0.000 8 | 0.000 11 | 0.105 9 | 0.000 6 | 0.000 9 | 0.483 3 | 0.000 11 | 0.000 7 | 0.028 7 | 0.000 2 | 0.000 3 | 0.906 1 | 0.000 1 | 0.339 14 | 0.000 1 | 0.000 12 | 0.457 9 | 0.000 1 | 0.612 7 | 0.000 1 | 0.000 1 | 0.408 3 | 0.000 14 | 0.900 9 | 0.000 8 | 0.000 8 | 0.000 1 | 0.029 4 | 0.000 1 | 0.074 15 | 0.455 14 | 0.479 6 | 0.427 6 | 0.079 9 | 0.140 9 | 0.496 7 | 0.414 13 | 0.022 6 | 0.000 1 | 0.471 13 | 0.000 2 | 0.000 2 | 0.000 11 | 0.722 7 | 0.000 2 | 0.000 1 | 0.000 1 | 0.138 12 | 0.000 8 | 0.000 4 | 0.000 10 | 0.000 1 | |||||||||||||||||||||||||||||
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
IMFSegNet | 0.337 8 | 0.535 11 | 0.266 10 | 0.169 8 | 0.810 8 | 0.715 2 | 0.947 6 | 0.545 8 | 0.675 5 | 0.759 5 | 0.418 9 | 0.406 6 | 0.493 11 | 0.000 11 | 0.803 7 | 0.571 9 | 0.755 5 | 0.264 9 | 0.644 12 | 0.953 5 | 0.366 16 | 0.840 13 | 0.831 12 | 0.357 3 | 0.564 9 | 0.317 11 | 0.699 11 | 0.422 11 | 0.633 7 | 0.509 9 | 0.424 5 | 0.824 4 | 0.637 5 | 0.001 16 | 0.382 6 | 0.383 14 | 0.000 1 | 0.810 8 | 0.884 5 | 0.678 15 | 0.311 3 | 0.000 7 | 0.469 13 | 0.072 11 | 0.527 3 | 0.300 8 | 0.576 9 | 0.769 7 | 0.177 15 | 0.181 5 | 0.829 8 | 0.642 11 | 0.095 6 | 0.791 5 | 0.655 6 | 0.645 8 | 0.524 8 | 0.692 8 | 0.208 10 | 0.000 3 | 0.264 3 | 0.398 10 | 0.867 4 | 0.343 11 | 0.915 5 | 0.000 4 | 0.036 16 | 0.551 14 | 0.442 9 | 0.432 13 | 0.568 1 | 0.000 1 | 0.359 10 | 0.660 9 | 0.000 4 | 0.040 5 | 0.000 1 | 0.000 6 | 0.853 4 | 0.821 10 | 0.469 11 | 0.546 14 | 0.361 9 | 0.000 15 | 0.683 5 | 0.373 10 | 0.000 3 | 0.000 7 | 0.000 1 | 0.038 3 | 0.000 4 | 0.054 12 | 0.472 16 | 0.000 1 | 0.026 7 | 0.000 10 | 0.868 2 | 0.708 4 | 0.341 8 | 0.263 5 | 0.170 11 | 0.263 4 | 0.109 7 | 0.426 10 | 0.228 12 | 0.143 5 | 0.000 5 | 0.251 5 | 0.442 7 | 0.000 1 | 0.447 5 | 0.187 6 | 0.544 4 | 0.311 13 | 0.396 12 | 0.728 3 | 0.211 3 | 0.066 12 | 0.147 12 | 0.000 1 | 0.505 12 | 0.378 11 | 0.743 4 | 0.000 1 | 0.000 8 | 0.204 6 | 0.118 4 | 0.000 6 | 0.000 9 | 0.297 9 | 0.122 5 | 0.133 4 | 0.004 10 | 0.032 1 | 0.667 1 | 0.839 9 | 0.000 1 | 0.380 10 | 0.000 1 | 0.051 9 | 0.492 7 | 0.000 1 | 0.572 10 | 0.000 1 | 0.000 1 | 0.196 10 | 0.004 11 | 0.910 8 | 0.000 8 | 0.000 8 | 0.000 1 | 0.000 6 | 0.000 1 | 0.112 5 | 0.544 5 | 0.497 4 | 0.152 15 | 0.095 2 | 0.132 15 | 0.277 16 | 0.634 5 | 0.031 4 | 0.000 1 | 0.592 3 | 0.000 2 | 0.000 2 | 0.119 7 | 0.786 5 | 0.000 2 | 0.000 1 | 0.000 1 | 0.126 15 | 0.177 2 | 0.094 2 | 0.274 8 | 0.000 1 | |||||||||||||||||||||||||||||
PonderV2 ScanNet200 | 0.346 5 | 0.552 7 | 0.270 8 | 0.175 6 | 0.810 8 | 0.682 9 | 0.950 4 | 0.560 6 | 0.641 11 | 0.761 4 | 0.398 11 | 0.357 10 | 0.570 7 | 0.113 2 | 0.804 5 | 0.603 6 | 0.750 7 | 0.283 4 | 0.681 7 | 0.952 6 | 0.548 5 | 0.874 4 | 0.852 11 | 0.290 10 | 0.700 2 | 0.356 10 | 0.792 4 | 0.445 9 | 0.545 12 | 0.436 11 | 0.351 12 | 0.787 9 | 0.611 9 | 0.050 9 | 0.290 13 | 0.519 11 | 0.000 1 | 0.825 7 | 0.888 4 | 0.842 3 | 0.259 5 | 0.100 2 | 0.558 6 | 0.070 13 | 0.497 8 | 0.247 13 | 0.457 12 | 0.889 3 | 0.248 9 | 0.106 11 | 0.817 11 | 0.691 6 | 0.094 7 | 0.729 6 | 0.636 7 | 0.620 12 | 0.503 11 | 0.660 12 | 0.243 6 | 0.000 3 | 0.212 8 | 0.590 4 | 0.860 8 | 0.400 5 | 0.881 8 | 0.000 4 | 0.202 2 | 0.622 10 | 0.408 10 | 0.499 8 | 0.261 10 | 0.000 1 | 0.385 9 | 0.636 10 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.433 15 | 0.843 7 | 0.660 6 | 0.574 12 | 0.481 3 | 0.336 4 | 0.677 7 | 0.486 5 | 0.000 3 | 0.030 3 | 0.000 1 | 0.034 6 | 0.000 4 | 0.080 7 | 0.869 9 | 0.000 1 | 0.000 10 | 0.000 10 | 0.540 9 | 0.727 3 | 0.232 15 | 0.115 9 | 0.186 8 | 0.193 9 | 0.000 14 | 0.403 11 | 0.326 6 | 0.103 12 | 0.000 5 | 0.290 4 | 0.392 10 | 0.000 1 | 0.346 8 | 0.062 10 | 0.424 6 | 0.375 7 | 0.431 5 | 0.667 5 | 0.115 13 | 0.082 10 | 0.239 7 | 0.000 1 | 0.504 13 | 0.606 6 | 0.584 11 | 0.000 1 | 0.002 6 | 0.186 8 | 0.104 10 | 0.000 6 | 0.394 4 | 0.384 7 | 0.083 8 | 0.000 7 | 0.007 8 | 0.000 2 | 0.000 3 | 0.880 4 | 0.000 1 | 0.377 11 | 0.000 1 | 0.263 3 | 0.565 2 | 0.000 1 | 0.608 8 | 0.000 1 | 0.000 1 | 0.304 7 | 0.009 8 | 0.924 3 | 0.000 8 | 0.000 8 | 0.000 1 | 0.000 6 | 0.000 1 | 0.128 3 | 0.584 3 | 0.475 7 | 0.412 7 | 0.076 10 | 0.269 3 | 0.621 5 | 0.509 8 | 0.010 7 | 0.000 1 | 0.491 10 | 0.063 1 | 0.000 2 | 0.472 4 | 0.880 2 | 0.000 2 | 0.000 1 | 0.000 1 | 0.179 5 | 0.125 3 | 0.000 4 | 0.441 6 | 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 11 | 0.556 5 | 0.270 7 | 0.123 13 | 0.816 6 | 0.682 9 | 0.946 8 | 0.549 7 | 0.657 10 | 0.756 7 | 0.459 6 | 0.376 9 | 0.550 8 | 0.001 10 | 0.807 4 | 0.616 4 | 0.727 11 | 0.267 7 | 0.691 6 | 0.942 10 | 0.530 10 | 0.872 5 | 0.874 8 | 0.330 8 | 0.542 13 | 0.374 7 | 0.792 4 | 0.400 13 | 0.673 4 | 0.572 7 | 0.433 2 | 0.793 8 | 0.623 8 | 0.008 15 | 0.351 9 | 0.594 7 | 0.000 1 | 0.783 12 | 0.876 8 | 0.833 4 | 0.213 6 | 0.000 7 | 0.537 7 | 0.091 6 | 0.519 5 | 0.304 7 | 0.620 7 | 0.942 1 | 0.264 5 | 0.124 9 | 0.855 6 | 0.695 5 | 0.086 8 | 0.646 10 | 0.506 14 | 0.658 6 | 0.535 6 | 0.715 4 | 0.314 2 | 0.000 3 | 0.241 6 | 0.608 2 | 0.897 2 | 0.359 8 | 0.858 10 | 0.000 4 | 0.076 15 | 0.611 11 | 0.392 11 | 0.509 7 | 0.378 5 | 0.000 1 | 0.579 4 | 0.565 15 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.755 8 | 0.806 11 | 0.661 4 | 0.572 13 | 0.350 10 | 0.181 7 | 0.660 10 | 0.300 13 | 0.000 3 | 0.000 7 | 0.000 1 | 0.023 9 | 0.000 4 | 0.042 15 | 0.930 4 | 0.000 1 | 0.000 10 | 0.077 7 | 0.584 8 | 0.392 10 | 0.339 9 | 0.185 8 | 0.171 10 | 0.308 2 | 0.006 13 | 0.563 3 | 0.256 9 | 0.150 4 | 0.000 5 | 0.002 15 | 0.345 13 | 0.000 1 | 0.045 13 | 0.197 4 | 0.063 10 | 0.323 11 | 0.453 3 | 0.600 9 | 0.163 9 | 0.037 14 | 0.349 4 | 0.000 1 | 0.672 3 | 0.679 4 | 0.753 3 | 0.000 1 | 0.000 8 | 0.000 11 | 0.117 6 | 0.000 6 | 0.000 9 | 0.291 10 | 0.000 11 | 0.000 7 | 0.039 6 | 0.000 2 | 0.000 3 | 0.899 2 | 0.000 1 | 0.374 12 | 0.000 1 | 0.000 12 | 0.545 4 | 0.000 1 | 0.634 5 | 0.000 1 | 0.000 1 | 0.074 11 | 0.223 4 | 0.914 7 | 0.000 8 | 0.021 6 | 0.000 1 | 0.000 6 | 0.000 1 | 0.112 5 | 0.498 11 | 0.649 1 | 0.383 9 | 0.095 2 | 0.135 13 | 0.449 10 | 0.432 11 | 0.008 9 | 0.000 1 | 0.518 7 | 0.000 2 | 0.000 2 | 0.000 11 | 0.796 4 | 0.000 2 | 0.000 1 | 0.000 1 | 0.138 12 | 0.000 8 | 0.000 4 | 0.000 10 | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CSC-Pretrain | 0.249 16 | 0.455 16 | 0.171 15 | 0.079 16 | 0.766 16 | 0.659 14 | 0.930 16 | 0.494 13 | 0.542 16 | 0.700 16 | 0.314 16 | 0.215 16 | 0.430 16 | 0.121 1 | 0.697 16 | 0.441 15 | 0.683 15 | 0.235 13 | 0.609 16 | 0.895 15 | 0.476 15 | 0.816 15 | 0.770 16 | 0.186 13 | 0.634 5 | 0.216 16 | 0.734 8 | 0.340 15 | 0.471 15 | 0.307 15 | 0.293 16 | 0.591 16 | 0.542 14 | 0.076 7 | 0.205 15 | 0.464 13 | 0.000 1 | 0.484 16 | 0.832 16 | 0.766 7 | 0.052 14 | 0.000 7 | 0.413 15 | 0.059 15 | 0.418 15 | 0.222 15 | 0.318 16 | 0.609 14 | 0.206 13 | 0.112 10 | 0.743 12 | 0.625 13 | 0.076 10 | 0.579 15 | 0.548 11 | 0.590 15 | 0.371 15 | 0.552 16 | 0.081 15 | 0.003 2 | 0.142 13 | 0.201 15 | 0.638 16 | 0.233 15 | 0.686 16 | 0.000 4 | 0.142 8 | 0.444 16 | 0.375 12 | 0.247 16 | 0.198 13 | 0.000 1 | 0.128 16 | 0.454 16 | 0.019 2 | 0.097 1 | 0.000 1 | 0.000 6 | 0.553 13 | 0.557 15 | 0.373 12 | 0.545 15 | 0.164 14 | 0.014 14 | 0.547 15 | 0.174 14 | 0.000 3 | 0.002 5 | 0.000 1 | 0.037 4 | 0.000 4 | 0.063 10 | 0.664 14 | 0.000 1 | 0.000 10 | 0.130 2 | 0.170 13 | 0.152 16 | 0.335 10 | 0.079 12 | 0.110 14 | 0.175 13 | 0.098 9 | 0.175 16 | 0.166 14 | 0.045 16 | 0.207 2 | 0.014 13 | 0.465 5 | 0.000 1 | 0.001 16 | 0.001 16 | 0.046 11 | 0.299 14 | 0.327 15 | 0.537 12 | 0.033 15 | 0.012 16 | 0.186 10 | 0.000 1 | 0.205 15 | 0.377 13 | 0.463 15 | 0.000 1 | 0.058 3 | 0.000 11 | 0.055 14 | 0.041 2 | 0.000 9 | 0.105 15 | 0.000 11 | 0.000 7 | 0.000 11 | 0.000 2 | 0.000 3 | 0.398 14 | 0.000 1 | 0.308 16 | 0.000 1 | 0.000 12 | 0.319 14 | 0.000 1 | 0.543 13 | 0.000 1 | 0.000 1 | 0.062 13 | 0.004 11 | 0.862 15 | 0.000 8 | 0.000 8 | 0.000 1 | 0.000 6 | 0.000 1 | 0.123 4 | 0.316 15 | 0.225 14 | 0.250 12 | 0.094 4 | 0.180 6 | 0.332 14 | 0.441 10 | 0.000 12 | 0.000 1 | 0.310 16 | 0.000 2 | 0.000 2 | 0.000 11 | 0.592 12 | 0.000 2 | 0.000 1 | 0.000 1 | 0.203 2 | 0.000 8 | 0.000 4 | 0.000 10 | 0.000 1 | |||||||||||||||||||||||||||||
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
AWCS | 0.305 13 | 0.508 13 | 0.225 13 | 0.142 10 | 0.782 13 | 0.634 16 | 0.937 13 | 0.489 14 | 0.578 13 | 0.721 11 | 0.364 14 | 0.355 11 | 0.515 9 | 0.023 7 | 0.764 13 | 0.523 12 | 0.707 13 | 0.264 9 | 0.633 13 | 0.922 12 | 0.507 13 | 0.886 1 | 0.804 14 | 0.179 14 | 0.436 15 | 0.300 13 | 0.656 15 | 0.529 2 | 0.501 14 | 0.394 12 | 0.296 15 | 0.820 6 | 0.603 10 | 0.131 4 | 0.179 16 | 0.619 2 | 0.000 1 | 0.707 15 | 0.865 13 | 0.773 6 | 0.171 7 | 0.010 6 | 0.484 12 | 0.063 14 | 0.463 13 | 0.254 12 | 0.332 15 | 0.649 12 | 0.220 11 | 0.100 12 | 0.729 13 | 0.613 14 | 0.071 12 | 0.582 14 | 0.628 8 | 0.702 4 | 0.424 14 | 0.749 2 | 0.137 14 | 0.000 3 | 0.142 13 | 0.360 11 | 0.863 6 | 0.305 13 | 0.877 9 | 0.000 4 | 0.173 3 | 0.606 12 | 0.337 13 | 0.478 10 | 0.154 14 | 0.000 1 | 0.253 13 | 0.664 7 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.626 12 | 0.782 12 | 0.302 15 | 0.602 6 | 0.185 13 | 0.282 6 | 0.651 11 | 0.317 12 | 0.000 3 | 0.000 7 | 0.000 1 | 0.022 10 | 0.000 4 | 0.154 1 | 0.876 8 | 0.000 1 | 0.014 9 | 0.063 9 | 0.029 16 | 0.553 7 | 0.467 3 | 0.084 11 | 0.124 13 | 0.157 15 | 0.049 12 | 0.373 13 | 0.252 10 | 0.097 13 | 0.000 5 | 0.219 6 | 0.542 3 | 0.000 1 | 0.392 6 | 0.172 8 | 0.000 14 | 0.339 9 | 0.417 8 | 0.533 13 | 0.093 14 | 0.115 8 | 0.195 9 | 0.000 1 | 0.516 10 | 0.288 15 | 0.741 5 | 0.000 1 | 0.001 7 | 0.233 5 | 0.056 13 | 0.000 6 | 0.159 5 | 0.334 8 | 0.077 9 | 0.000 7 | 0.000 11 | 0.000 2 | 0.000 3 | 0.749 12 | 0.000 1 | 0.411 8 | 0.000 1 | 0.008 11 | 0.452 10 | 0.000 1 | 0.595 9 | 0.000 1 | 0.000 1 | 0.220 9 | 0.006 9 | 0.894 11 | 0.006 6 | 0.000 8 | 0.000 1 | 0.000 6 | 0.000 1 | 0.112 5 | 0.504 9 | 0.404 10 | 0.551 1 | 0.093 5 | 0.129 16 | 0.484 8 | 0.381 16 | 0.000 12 | 0.000 1 | 0.396 14 | 0.000 2 | 0.000 2 | 0.620 3 | 0.402 16 | 0.000 2 | 0.000 1 | 0.000 1 | 0.142 9 | 0.000 8 | 0.000 4 | 0.512 5 | 0.000 1 | |||||||||||||||||||||||||||||
CeCo | 0.340 6 | 0.551 8 | 0.247 12 | 0.181 5 | 0.784 12 | 0.661 13 | 0.939 12 | 0.564 5 | 0.624 12 | 0.721 11 | 0.484 4 | 0.429 4 | 0.575 4 | 0.027 6 | 0.774 11 | 0.503 13 | 0.753 6 | 0.242 12 | 0.656 11 | 0.945 8 | 0.534 8 | 0.865 6 | 0.860 9 | 0.177 16 | 0.616 7 | 0.400 4 | 0.818 2 | 0.579 1 | 0.615 10 | 0.367 13 | 0.408 7 | 0.726 14 | 0.633 6 | 0.162 1 | 0.360 8 | 0.619 2 | 0.000 1 | 0.828 6 | 0.873 11 | 0.924 2 | 0.109 11 | 0.083 3 | 0.564 5 | 0.057 16 | 0.475 12 | 0.266 10 | 0.781 1 | 0.767 8 | 0.257 7 | 0.100 12 | 0.825 9 | 0.663 10 | 0.048 14 | 0.620 13 | 0.551 10 | 0.595 14 | 0.532 7 | 0.692 8 | 0.246 5 | 0.000 3 | 0.213 7 | 0.615 1 | 0.861 7 | 0.376 7 | 0.900 6 | 0.000 4 | 0.102 13 | 0.660 7 | 0.321 14 | 0.547 4 | 0.226 12 | 0.000 1 | 0.311 12 | 0.742 3 | 0.011 3 | 0.006 8 | 0.000 1 | 0.000 6 | 0.546 14 | 0.824 9 | 0.345 13 | 0.665 3 | 0.450 5 | 0.435 1 | 0.683 5 | 0.411 8 | 0.338 1 | 0.000 7 | 0.000 1 | 0.030 7 | 0.000 4 | 0.068 8 | 0.892 7 | 0.000 1 | 0.063 4 | 0.000 10 | 0.257 12 | 0.304 13 | 0.387 5 | 0.079 12 | 0.228 6 | 0.190 11 | 0.000 14 | 0.586 1 | 0.347 4 | 0.133 8 | 0.000 5 | 0.037 12 | 0.377 11 | 0.000 1 | 0.384 7 | 0.006 15 | 0.003 12 | 0.421 3 | 0.410 10 | 0.643 7 | 0.171 7 | 0.121 7 | 0.142 13 | 0.000 1 | 0.510 11 | 0.447 9 | 0.474 13 | 0.000 1 | 0.000 8 | 0.286 3 | 0.083 12 | 0.000 6 | 0.000 9 | 0.603 1 | 0.096 7 | 0.063 6 | 0.000 11 | 0.000 2 | 0.000 3 | 0.898 3 | 0.000 1 | 0.429 7 | 0.000 1 | 0.400 1 | 0.550 3 | 0.000 1 | 0.633 6 | 0.000 1 | 0.000 1 | 0.377 4 | 0.000 14 | 0.916 5 | 0.000 8 | 0.000 8 | 0.000 1 | 0.000 6 | 0.000 1 | 0.102 12 | 0.499 10 | 0.296 13 | 0.463 5 | 0.089 6 | 0.304 1 | 0.740 2 | 0.401 15 | 0.010 7 | 0.000 1 | 0.560 4 | 0.000 2 | 0.000 2 | 0.709 2 | 0.652 10 | 0.000 2 | 0.000 1 | 0.000 1 | 0.143 8 | 0.000 8 | 0.000 4 | 0.609 3 | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
LGround | 0.272 14 | 0.485 14 | 0.184 14 | 0.106 14 | 0.778 14 | 0.676 11 | 0.932 14 | 0.479 16 | 0.572 14 | 0.718 13 | 0.399 10 | 0.265 13 | 0.453 15 | 0.085 3 | 0.745 14 | 0.446 14 | 0.726 12 | 0.232 14 | 0.622 14 | 0.901 14 | 0.512 12 | 0.826 14 | 0.786 15 | 0.178 15 | 0.549 11 | 0.277 14 | 0.659 14 | 0.381 14 | 0.518 13 | 0.295 16 | 0.323 13 | 0.777 11 | 0.599 11 | 0.028 11 | 0.321 10 | 0.363 15 | 0.000 1 | 0.708 14 | 0.858 14 | 0.746 10 | 0.063 13 | 0.022 5 | 0.457 14 | 0.077 9 | 0.476 11 | 0.243 14 | 0.402 13 | 0.397 16 | 0.233 10 | 0.077 16 | 0.720 15 | 0.610 15 | 0.103 5 | 0.629 12 | 0.437 16 | 0.626 11 | 0.446 13 | 0.702 6 | 0.190 11 | 0.005 1 | 0.058 15 | 0.322 12 | 0.702 15 | 0.244 14 | 0.768 13 | 0.000 4 | 0.134 10 | 0.552 13 | 0.279 15 | 0.395 14 | 0.147 15 | 0.000 1 | 0.207 14 | 0.612 13 | 0.000 4 | 0.000 9 | 0.000 1 | 0.000 6 | 0.658 10 | 0.566 14 | 0.323 14 | 0.525 16 | 0.229 12 | 0.179 8 | 0.467 16 | 0.154 15 | 0.000 3 | 0.002 5 | 0.000 1 | 0.051 1 | 0.000 4 | 0.127 2 | 0.703 11 | 0.000 1 | 0.000 10 | 0.216 1 | 0.112 15 | 0.358 11 | 0.547 1 | 0.187 7 | 0.092 15 | 0.156 16 | 0.055 10 | 0.296 14 | 0.252 10 | 0.143 5 | 0.000 5 | 0.014 13 | 0.398 9 | 0.000 1 | 0.028 15 | 0.173 7 | 0.000 14 | 0.265 15 | 0.348 14 | 0.415 15 | 0.179 6 | 0.019 15 | 0.218 8 | 0.000 1 | 0.597 7 | 0.274 16 | 0.565 12 | 0.000 1 | 0.012 5 | 0.000 11 | 0.039 15 | 0.022 3 | 0.000 9 | 0.117 14 | 0.000 11 | 0.000 7 | 0.000 11 | 0.000 2 | 0.000 3 | 0.324 15 | 0.000 1 | 0.384 9 | 0.000 1 | 0.000 12 | 0.251 16 | 0.000 1 | 0.566 11 | 0.000 1 | 0.000 1 | 0.066 12 | 0.404 1 | 0.886 12 | 0.199 2 | 0.000 8 | 0.000 1 | 0.059 3 | 0.000 1 | 0.136 1 | 0.540 6 | 0.127 16 | 0.295 10 | 0.085 7 | 0.143 7 | 0.514 6 | 0.413 14 | 0.000 12 | 0.000 1 | 0.498 9 | 0.000 2 | 0.000 2 | 0.000 11 | 0.623 11 | 0.000 2 | 0.000 1 | 0.000 1 | 0.132 14 | 0.000 8 | 0.000 4 | 0.000 10 | 0.000 1 | |||||||||||||||||||||||||||||
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Minkowski 34D | 0.253 15 | 0.463 15 | 0.154 16 | 0.102 15 | 0.771 15 | 0.650 15 | 0.932 14 | 0.483 15 | 0.571 15 | 0.710 14 | 0.331 15 | 0.250 14 | 0.492 12 | 0.044 4 | 0.703 15 | 0.419 16 | 0.606 16 | 0.227 15 | 0.621 15 | 0.865 16 | 0.531 9 | 0.771 16 | 0.813 13 | 0.291 9 | 0.484 14 | 0.242 15 | 0.612 16 | 0.282 16 | 0.440 16 | 0.351 14 | 0.299 14 | 0.622 15 | 0.593 12 | 0.027 12 | 0.293 12 | 0.310 16 | 0.000 1 | 0.757 13 | 0.858 14 | 0.737 12 | 0.150 8 | 0.164 1 | 0.368 16 | 0.084 7 | 0.381 16 | 0.142 16 | 0.357 14 | 0.720 10 | 0.214 12 | 0.092 15 | 0.724 14 | 0.596 16 | 0.056 13 | 0.655 9 | 0.525 12 | 0.581 16 | 0.352 16 | 0.594 15 | 0.056 16 | 0.000 3 | 0.014 16 | 0.224 14 | 0.772 14 | 0.205 16 | 0.720 15 | 0.000 4 | 0.159 5 | 0.531 15 | 0.163 16 | 0.294 15 | 0.136 16 | 0.000 1 | 0.169 15 | 0.589 14 | 0.000 4 | 0.000 9 | 0.000 1 | 0.002 4 | 0.663 9 | 0.466 16 | 0.265 16 | 0.582 10 | 0.337 11 | 0.016 13 | 0.559 14 | 0.084 16 | 0.000 3 | 0.000 7 | 0.000 1 | 0.036 5 | 0.000 4 | 0.125 3 | 0.670 12 | 0.000 1 | 0.102 2 | 0.071 8 | 0.164 14 | 0.406 9 | 0.386 6 | 0.046 14 | 0.068 16 | 0.159 14 | 0.117 5 | 0.284 15 | 0.111 15 | 0.094 14 | 0.000 5 | 0.000 16 | 0.197 16 | 0.000 1 | 0.044 14 | 0.013 13 | 0.002 13 | 0.228 16 | 0.307 16 | 0.588 11 | 0.025 16 | 0.545 5 | 0.134 14 | 0.000 1 | 0.655 4 | 0.302 14 | 0.282 16 | 0.000 1 | 0.060 2 | 0.000 11 | 0.035 16 | 0.000 6 | 0.000 9 | 0.097 16 | 0.000 11 | 0.000 7 | 0.005 9 | 0.000 2 | 0.000 3 | 0.096 16 | 0.000 1 | 0.334 15 | 0.000 1 | 0.000 12 | 0.274 15 | 0.000 1 | 0.513 15 | 0.000 1 | 0.000 1 | 0.280 8 | 0.194 5 | 0.897 10 | 0.000 8 | 0.000 8 | 0.000 1 | 0.000 6 | 0.000 1 | 0.108 10 | 0.279 16 | 0.189 15 | 0.141 16 | 0.059 14 | 0.272 2 | 0.307 15 | 0.445 9 | 0.003 10 | 0.000 1 | 0.353 15 | 0.000 2 | 0.026 1 | 0.000 11 | 0.581 13 | 0.001 1 | 0.000 1 | 0.000 1 | 0.093 16 | 0.002 7 | 0.000 4 | 0.000 10 | 0.000 1 | |||||||||||||||||||||||||||||
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 |