ScanNet200 3D Semantic Label Benchmark
The 3D semantic labeling task involves predicting a semantic labeling of a 3D scan mesh.
Evaluation and metricsOur evaluation ranks all methods according to the PASCAL VOC intersection-over-union metric (IoU). IoU = TP/(TP+FP+FN), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively. Predicted labels are evaluated per-vertex over the respective 3D scan mesh; for 3D approaches that operate on other representations like grids or points, the predicted labels should be mapped onto the mesh vertices (e.g., one such example for grid to mesh vertices is provided in the evaluation helpers).
This table lists the benchmark results for the ScanNet200 3D semantic label scenario.
Method | Info | avg iou | head iou | common iou | tail iou | wall | chair | floor | table | door | couch | cabinet | shelf | desk | office chair | bed | pillow | sink | picture | window | toilet | bookshelf | monitor | curtain | book | armchair | coffee table | box | refrigerator | lamp | kitchen cabinet | towel | clothes | tv | nightstand | counter | dresser | stool | cushion | plant | ceiling | bathtub | end table | dining table | keyboard | bag | backpack | toilet paper | printer | tv stand | whiteboard | blanket | shower curtain | trash can | closet | stairs | microwave | stove | shoe | computer tower | bottle | bin | ottoman | bench | board | washing machine | mirror | copier | basket | sofa chair | file cabinet | fan | laptop | shower | paper | person | paper towel dispenser | oven | blinds | rack | plate | blackboard | piano | suitcase | rail | radiator | recycling bin | container | wardrobe | soap dispenser | telephone | bucket | clock | stand | light | laundry basket | pipe | clothes dryer | guitar | toilet paper holder | seat | speaker | column | bicycle | ladder | bathroom stall | shower wall | cup | jacket | storage bin | coffee maker | dishwasher | paper towel roll | machine | mat | windowsill | bar | toaster | bulletin board | ironing board | fireplace | soap dish | kitchen counter | doorframe | toilet paper dispenser | mini fridge | fire extinguisher | ball | hat | shower curtain rod | water cooler | paper cutter | tray | shower door | pillar | ledge | toaster oven | mouse | toilet seat cover dispenser | furniture | cart | storage container | scale | tissue box | light switch | crate | power outlet | decoration | sign | projector | closet door | vacuum cleaner | candle | plunger | stuffed animal | headphones | dish rack | broom | guitar case | range hood | dustpan | hair dryer | water bottle | handicap bar | purse | vent | shower floor | water pitcher | mailbox | bowl | paper bag | alarm clock | music stand | projector screen | divider | laundry detergent | bathroom counter | object | bathroom vanity | closet wall | laundry hamper | bathroom stall door | ceiling light | trash bin | dumbbell | stair rail | tube | bathroom cabinet | cd case | closet rod | coffee kettle | structure | shower head | keyboard piano | case of water bottles | coat rack | storage organizer | folded chair | fire alarm | power strip | calendar | poster | potted plant | luggage | mattress |
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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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||
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) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||
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 |