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