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