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 | 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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CeCo | 0.340 1 | 0.551 3 | 0.247 4 | 0.181 1 | 0.475 4 | 0.057 8 | 0.142 7 | 0.000 1 | 0.000 1 | 0.000 1 | 0.387 3 | 0.463 3 | 0.499 4 | 0.924 1 | 0.774 3 | 0.213 2 | 0.257 4 | 0.000 3 | 0.546 8 | 0.100 5 | 0.006 3 | 0.615 1 | 0.177 8 | 0.534 2 | 0.246 3 | 0.000 4 | 0.400 2 | 0.000 1 | 0.338 1 | 0.006 7 | 0.484 2 | 0.609 1 | 0.000 1 | 0.083 4 | 0.000 2 | 0.873 3 | 0.089 4 | 0.661 5 | 0.000 3 | 0.048 8 | 0.560 1 | 0.408 3 | 0.892 3 | 0.000 1 | 0.000 1 | 0.586 1 | 0.616 3 | 0.000 7 | 0.692 4 | 0.900 1 | 0.721 3 | 0.162 1 | 0.228 1 | 0.860 3 | 0.000 1 | 0.000 4 | 0.575 1 | 0.083 2 | 0.550 1 | 0.347 1 | 0.624 4 | 0.410 4 | 0.360 2 | 0.740 1 | 0.109 5 | 0.321 6 | 0.660 2 | 0.000 2 | 0.121 2 | 0.939 4 | 0.143 3 | 0.000 1 | 0.400 1 | 0.003 5 | 0.190 3 | 0.564 1 | 0.652 4 | 0.615 4 | 0.421 1 | 0.304 6 | 0.579 1 | 0.547 1 | 0.000 2 | 0.000 1 | 0.296 5 | 0.000 5 | 0.030 4 | 0.096 1 | 0.000 3 | 0.916 1 | 0.037 4 | 0.551 4 | 0.171 3 | 0.376 2 | 0.865 4 | 0.286 1 | 0.000 1 | 0.633 1 | 0.102 7 | 0.027 4 | 0.011 3 | 0.000 1 | 0.000 3 | 0.474 5 | 0.742 1 | 0.133 3 | 0.311 4 | 0.824 3 | 0.242 4 | 0.503 5 | 0.068 4 | 0.828 1 | 0.000 3 | 0.429 2 | 0.000 1 | 0.063 1 | 0.000 2 | 0.781 1 | 0.000 1 | 0.000 4 | 0.000 1 | 0.665 1 | 0.633 2 | 0.450 1 | 0.818 1 | 0.000 3 | 0.000 1 | 0.429 2 | 0.532 3 | 0.226 4 | 0.825 3 | 0.510 6 | 0.377 2 | 0.709 1 | 0.079 6 | 0.000 1 | 0.753 1 | 0.683 1 | 0.102 7 | 0.063 2 | 0.401 7 | 0.620 6 | 0.000 1 | 0.619 2 | 0.000 8 | 0.000 4 | 0.000 1 | 0.595 6 | 0.000 2 | 0.000 1 | 0.345 5 | 0.564 2 | 0.411 1 | 0.603 1 | 0.384 2 | 0.945 2 | 0.266 4 | 0.643 1 | 0.367 5 | 0.304 1 | 0.663 4 | 0.000 1 | 0.010 2 | 0.726 6 | 0.767 3 | 0.898 3 | 0.000 1 | 0.784 4 | 0.435 1 | 0.861 4 | 0.000 1 | 0.447 3 | 0.000 7 | 0.257 3 | 0.656 4 | 0.377 4 | |||||||||||||||||||||||||||||
: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
OA-CNN-L_ScanNet200 | 0.333 2 | 0.558 1 | 0.269 2 | 0.124 4 | 0.448 6 | 0.080 4 | 0.272 3 | 0.000 1 | 0.000 1 | 0.000 1 | 0.342 5 | 0.515 2 | 0.524 2 | 0.713 8 | 0.789 2 | 0.158 4 | 0.384 3 | 0.000 3 | 0.806 2 | 0.125 2 | 0.000 4 | 0.496 3 | 0.332 2 | 0.498 7 | 0.227 4 | 0.024 2 | 0.474 1 | 0.000 1 | 0.003 2 | 0.071 4 | 0.487 1 | 0.000 3 | 0.000 1 | 0.110 2 | 0.000 2 | 0.876 1 | 0.013 8 | 0.703 1 | 0.000 3 | 0.076 4 | 0.473 4 | 0.355 4 | 0.906 2 | 0.000 1 | 0.000 1 | 0.476 4 | 0.706 1 | 0.000 7 | 0.672 6 | 0.835 5 | 0.748 2 | 0.015 7 | 0.223 2 | 0.860 3 | 0.000 1 | 0.000 4 | 0.572 2 | 0.000 5 | 0.509 3 | 0.313 2 | 0.662 1 | 0.398 5 | 0.396 1 | 0.411 6 | 0.276 1 | 0.527 1 | 0.711 1 | 0.000 2 | 0.076 5 | 0.946 1 | 0.166 2 | 0.000 1 | 0.022 2 | 0.160 1 | 0.183 4 | 0.493 4 | 0.699 3 | 0.637 2 | 0.403 2 | 0.330 5 | 0.406 4 | 0.526 2 | 0.024 1 | 0.000 1 | 0.392 4 | 0.000 5 | 0.016 8 | 0.000 3 | 0.196 2 | 0.915 2 | 0.112 3 | 0.557 3 | 0.197 1 | 0.352 4 | 0.877 2 | 0.000 3 | 0.000 1 | 0.592 6 | 0.103 6 | 0.000 8 | 0.067 1 | 0.000 1 | 0.089 1 | 0.735 3 | 0.625 3 | 0.130 5 | 0.568 2 | 0.836 2 | 0.271 1 | 0.534 3 | 0.043 6 | 0.799 2 | 0.001 2 | 0.445 1 | 0.000 1 | 0.000 2 | 0.024 1 | 0.661 2 | 0.000 1 | 0.262 1 | 0.000 1 | 0.591 3 | 0.517 7 | 0.373 3 | 0.788 3 | 0.021 2 | 0.000 1 | 0.455 1 | 0.517 4 | 0.320 3 | 0.823 4 | 0.200 8 | 0.001 8 | 0.150 3 | 0.100 4 | 0.000 1 | 0.736 2 | 0.668 2 | 0.103 6 | 0.052 3 | 0.662 1 | 0.720 1 | 0.000 1 | 0.602 4 | 0.112 3 | 0.002 3 | 0.000 1 | 0.637 4 | 0.000 2 | 0.000 1 | 0.621 3 | 0.569 1 | 0.398 2 | 0.412 3 | 0.234 3 | 0.949 1 | 0.363 1 | 0.492 7 | 0.495 3 | 0.251 3 | 0.665 3 | 0.000 1 | 0.001 5 | 0.805 2 | 0.833 2 | 0.794 4 | 0.000 1 | 0.821 1 | 0.314 2 | 0.843 5 | 0.000 1 | 0.560 2 | 0.245 2 | 0.262 2 | 0.713 1 | 0.370 5 | |||||||||||||||||||||||||||||
PPT-SpUNet-F.T. | 0.332 3 | 0.556 2 | 0.270 1 | 0.123 5 | 0.519 1 | 0.091 2 | 0.349 2 | 0.000 1 | 0.000 1 | 0.000 1 | 0.339 6 | 0.383 5 | 0.498 5 | 0.833 2 | 0.807 1 | 0.241 1 | 0.584 2 | 0.000 3 | 0.755 3 | 0.124 3 | 0.000 4 | 0.608 2 | 0.330 3 | 0.530 4 | 0.314 1 | 0.000 4 | 0.374 3 | 0.000 1 | 0.000 3 | 0.197 1 | 0.459 3 | 0.000 3 | 0.000 1 | 0.117 1 | 0.000 2 | 0.876 1 | 0.095 1 | 0.682 2 | 0.000 3 | 0.086 3 | 0.518 2 | 0.433 1 | 0.930 1 | 0.000 1 | 0.000 1 | 0.563 3 | 0.542 5 | 0.077 4 | 0.715 2 | 0.858 3 | 0.756 1 | 0.008 8 | 0.171 4 | 0.874 2 | 0.000 1 | 0.039 1 | 0.550 3 | 0.000 5 | 0.545 2 | 0.256 3 | 0.657 3 | 0.453 1 | 0.351 3 | 0.449 5 | 0.213 2 | 0.392 3 | 0.611 4 | 0.000 2 | 0.037 6 | 0.946 1 | 0.138 5 | 0.000 1 | 0.000 4 | 0.063 3 | 0.308 1 | 0.537 2 | 0.796 1 | 0.673 1 | 0.323 5 | 0.392 3 | 0.400 5 | 0.509 3 | 0.000 2 | 0.000 1 | 0.649 1 | 0.000 5 | 0.023 5 | 0.000 3 | 0.000 3 | 0.914 3 | 0.002 7 | 0.506 7 | 0.163 5 | 0.359 3 | 0.872 3 | 0.000 3 | 0.000 1 | 0.623 2 | 0.112 3 | 0.001 7 | 0.000 4 | 0.000 1 | 0.021 2 | 0.753 1 | 0.565 7 | 0.150 1 | 0.579 1 | 0.806 4 | 0.267 2 | 0.616 1 | 0.042 7 | 0.783 4 | 0.000 3 | 0.374 5 | 0.000 1 | 0.000 2 | 0.000 2 | 0.620 4 | 0.000 1 | 0.000 4 | 0.000 1 | 0.572 6 | 0.634 1 | 0.350 4 | 0.792 2 | 0.000 3 | 0.000 1 | 0.376 4 | 0.535 2 | 0.378 2 | 0.855 1 | 0.672 1 | 0.074 5 | 0.000 4 | 0.185 3 | 0.000 1 | 0.727 3 | 0.660 3 | 0.076 8 | 0.000 6 | 0.432 4 | 0.646 3 | 0.000 1 | 0.594 5 | 0.006 7 | 0.000 4 | 0.000 1 | 0.658 2 | 0.000 2 | 0.000 1 | 0.661 1 | 0.549 3 | 0.300 5 | 0.291 5 | 0.045 5 | 0.942 4 | 0.304 2 | 0.600 3 | 0.572 2 | 0.135 7 | 0.695 1 | 0.000 1 | 0.008 3 | 0.793 3 | 0.942 1 | 0.899 2 | 0.000 1 | 0.816 2 | 0.181 4 | 0.897 1 | 0.000 1 | 0.679 1 | 0.223 3 | 0.264 1 | 0.691 2 | 0.345 6 | |||||||||||||||||||||||||||||
OctFormer ScanNet200 | ![]() | 0.326 4 | 0.539 4 | 0.265 3 | 0.131 3 | 0.499 2 | 0.110 1 | 0.522 1 | 0.000 1 | 0.000 1 | 0.000 1 | 0.318 8 | 0.427 4 | 0.455 6 | 0.743 6 | 0.765 4 | 0.175 3 | 0.842 1 | 0.000 3 | 0.828 1 | 0.204 1 | 0.033 2 | 0.429 4 | 0.335 1 | 0.601 1 | 0.312 2 | 0.000 4 | 0.357 4 | 0.000 1 | 0.000 3 | 0.047 5 | 0.423 4 | 0.000 3 | 0.000 1 | 0.105 3 | 0.000 2 | 0.873 3 | 0.079 6 | 0.670 4 | 0.000 3 | 0.117 1 | 0.471 5 | 0.432 2 | 0.829 5 | 0.000 1 | 0.000 1 | 0.584 2 | 0.417 8 | 0.089 3 | 0.684 5 | 0.837 4 | 0.705 7 | 0.021 6 | 0.178 3 | 0.892 1 | 0.000 1 | 0.028 2 | 0.505 5 | 0.000 5 | 0.457 4 | 0.200 6 | 0.662 1 | 0.412 3 | 0.244 6 | 0.496 3 | 0.000 8 | 0.451 2 | 0.626 3 | 0.000 2 | 0.102 4 | 0.943 3 | 0.138 5 | 0.000 1 | 0.000 4 | 0.149 2 | 0.291 2 | 0.534 3 | 0.722 2 | 0.632 3 | 0.331 4 | 0.253 7 | 0.453 3 | 0.487 4 | 0.000 2 | 0.000 1 | 0.479 2 | 0.000 5 | 0.022 6 | 0.000 3 | 0.000 3 | 0.900 4 | 0.128 2 | 0.684 1 | 0.164 4 | 0.413 1 | 0.854 5 | 0.000 3 | 0.000 1 | 0.512 8 | 0.074 8 | 0.003 6 | 0.000 4 | 0.000 1 | 0.000 3 | 0.469 6 | 0.613 4 | 0.132 4 | 0.529 3 | 0.871 1 | 0.227 7 | 0.582 2 | 0.026 8 | 0.787 3 | 0.000 3 | 0.339 6 | 0.000 1 | 0.000 2 | 0.000 2 | 0.626 3 | 0.000 1 | 0.029 3 | 0.000 1 | 0.587 4 | 0.612 3 | 0.411 2 | 0.724 5 | 0.000 3 | 0.000 1 | 0.407 3 | 0.552 1 | 0.513 1 | 0.849 2 | 0.655 2 | 0.408 1 | 0.000 4 | 0.296 1 | 0.000 1 | 0.686 6 | 0.645 5 | 0.145 3 | 0.022 4 | 0.414 5 | 0.633 4 | 0.000 1 | 0.637 1 | 0.224 1 | 0.000 4 | 0.000 1 | 0.650 3 | 0.000 2 | 0.000 1 | 0.622 2 | 0.535 4 | 0.343 3 | 0.483 2 | 0.230 4 | 0.943 3 | 0.289 3 | 0.618 2 | 0.596 1 | 0.140 6 | 0.679 2 | 0.000 1 | 0.022 1 | 0.783 4 | 0.620 6 | 0.906 1 | 0.000 1 | 0.806 3 | 0.137 6 | 0.865 2 | 0.000 1 | 0.378 4 | 0.000 7 | 0.168 8 | 0.680 3 | 0.227 7 | ||||||||||||||||||||||||||||
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
AWCS | 0.305 5 | 0.508 5 | 0.225 5 | 0.142 2 | 0.463 5 | 0.063 6 | 0.195 5 | 0.000 1 | 0.000 1 | 0.000 1 | 0.467 2 | 0.551 1 | 0.504 3 | 0.773 3 | 0.764 5 | 0.142 5 | 0.029 8 | 0.000 3 | 0.626 6 | 0.100 5 | 0.000 4 | 0.360 5 | 0.179 6 | 0.507 6 | 0.137 6 | 0.006 3 | 0.300 5 | 0.000 1 | 0.000 3 | 0.172 3 | 0.364 6 | 0.512 2 | 0.000 1 | 0.056 5 | 0.000 2 | 0.865 5 | 0.093 3 | 0.634 8 | 0.000 3 | 0.071 6 | 0.396 6 | 0.296 7 | 0.876 4 | 0.000 1 | 0.000 1 | 0.373 5 | 0.436 7 | 0.063 6 | 0.749 1 | 0.877 2 | 0.721 3 | 0.131 2 | 0.124 5 | 0.804 6 | 0.000 1 | 0.000 4 | 0.515 4 | 0.010 4 | 0.452 5 | 0.252 4 | 0.578 5 | 0.417 2 | 0.179 8 | 0.484 4 | 0.171 3 | 0.337 5 | 0.606 5 | 0.000 2 | 0.115 3 | 0.937 5 | 0.142 4 | 0.000 1 | 0.008 3 | 0.000 7 | 0.157 7 | 0.484 5 | 0.402 8 | 0.501 6 | 0.339 3 | 0.553 1 | 0.529 2 | 0.478 5 | 0.000 2 | 0.000 1 | 0.404 3 | 0.001 4 | 0.022 6 | 0.077 2 | 0.000 3 | 0.894 6 | 0.219 1 | 0.628 2 | 0.093 6 | 0.305 5 | 0.886 1 | 0.233 2 | 0.000 1 | 0.603 3 | 0.112 3 | 0.023 5 | 0.000 4 | 0.000 1 | 0.000 3 | 0.741 2 | 0.664 2 | 0.097 6 | 0.253 5 | 0.782 5 | 0.264 3 | 0.523 4 | 0.154 1 | 0.707 7 | 0.000 3 | 0.411 3 | 0.000 1 | 0.000 2 | 0.000 2 | 0.332 7 | 0.000 1 | 0.000 4 | 0.000 1 | 0.602 2 | 0.595 4 | 0.185 7 | 0.656 7 | 0.159 1 | 0.000 1 | 0.355 5 | 0.424 6 | 0.154 6 | 0.729 6 | 0.516 5 | 0.220 4 | 0.620 2 | 0.084 5 | 0.000 1 | 0.707 5 | 0.651 4 | 0.173 1 | 0.014 5 | 0.381 8 | 0.582 7 | 0.000 1 | 0.619 2 | 0.049 6 | 0.000 4 | 0.000 1 | 0.702 1 | 0.000 2 | 0.000 1 | 0.302 7 | 0.489 6 | 0.317 4 | 0.334 4 | 0.392 1 | 0.922 5 | 0.254 5 | 0.533 6 | 0.394 4 | 0.129 8 | 0.613 6 | 0.000 1 | 0.000 6 | 0.820 1 | 0.649 5 | 0.749 5 | 0.000 1 | 0.782 5 | 0.282 3 | 0.863 3 | 0.000 1 | 0.288 7 | 0.006 5 | 0.220 5 | 0.633 5 | 0.542 1 | |||||||||||||||||||||||||||||
LGround | ![]() | 0.272 6 | 0.485 6 | 0.184 6 | 0.106 6 | 0.476 3 | 0.077 5 | 0.218 4 | 0.000 1 | 0.000 1 | 0.000 1 | 0.547 1 | 0.295 6 | 0.540 1 | 0.746 5 | 0.745 6 | 0.058 7 | 0.112 7 | 0.005 1 | 0.658 5 | 0.077 8 | 0.000 4 | 0.322 6 | 0.178 7 | 0.512 5 | 0.190 5 | 0.199 1 | 0.277 6 | 0.000 1 | 0.000 3 | 0.173 2 | 0.399 5 | 0.000 3 | 0.000 1 | 0.039 7 | 0.000 2 | 0.858 6 | 0.085 5 | 0.676 3 | 0.002 1 | 0.103 2 | 0.498 3 | 0.323 5 | 0.703 6 | 0.000 1 | 0.000 1 | 0.296 6 | 0.549 4 | 0.216 1 | 0.702 3 | 0.768 6 | 0.718 5 | 0.028 4 | 0.092 7 | 0.786 7 | 0.000 1 | 0.000 4 | 0.453 7 | 0.022 3 | 0.251 8 | 0.252 4 | 0.572 6 | 0.348 6 | 0.321 4 | 0.514 2 | 0.063 6 | 0.279 7 | 0.552 6 | 0.000 2 | 0.019 7 | 0.932 6 | 0.132 7 | 0.000 1 | 0.000 4 | 0.000 7 | 0.156 8 | 0.457 6 | 0.623 5 | 0.518 5 | 0.265 7 | 0.358 4 | 0.381 6 | 0.395 6 | 0.000 2 | 0.000 1 | 0.127 8 | 0.012 3 | 0.051 1 | 0.000 3 | 0.000 3 | 0.886 7 | 0.014 5 | 0.437 8 | 0.179 2 | 0.244 6 | 0.826 6 | 0.000 3 | 0.000 1 | 0.599 4 | 0.136 1 | 0.085 2 | 0.000 4 | 0.000 1 | 0.000 3 | 0.565 4 | 0.612 5 | 0.143 2 | 0.207 6 | 0.566 6 | 0.232 6 | 0.446 6 | 0.127 2 | 0.708 6 | 0.000 3 | 0.384 4 | 0.000 1 | 0.000 2 | 0.000 2 | 0.402 5 | 0.000 1 | 0.059 2 | 0.000 1 | 0.525 8 | 0.566 5 | 0.229 6 | 0.659 6 | 0.000 3 | 0.000 1 | 0.265 6 | 0.446 5 | 0.147 7 | 0.720 8 | 0.597 4 | 0.066 6 | 0.000 4 | 0.187 2 | 0.000 1 | 0.726 4 | 0.467 8 | 0.134 5 | 0.000 6 | 0.413 6 | 0.629 5 | 0.000 1 | 0.363 7 | 0.055 5 | 0.022 2 | 0.000 1 | 0.626 5 | 0.000 2 | 0.000 1 | 0.323 6 | 0.479 8 | 0.154 7 | 0.117 6 | 0.028 7 | 0.901 6 | 0.243 6 | 0.415 8 | 0.295 8 | 0.143 5 | 0.610 7 | 0.000 1 | 0.000 6 | 0.777 5 | 0.397 8 | 0.324 7 | 0.000 1 | 0.778 6 | 0.179 5 | 0.702 7 | 0.000 1 | 0.274 8 | 0.404 1 | 0.233 4 | 0.622 6 | 0.398 3 | ||||||||||||||||||||||||||||
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CSC-Pretrain | ![]() | 0.249 8 | 0.455 8 | 0.171 7 | 0.079 8 | 0.418 7 | 0.059 7 | 0.186 6 | 0.000 1 | 0.000 1 | 0.000 1 | 0.335 7 | 0.250 7 | 0.316 7 | 0.766 4 | 0.697 8 | 0.142 5 | 0.170 5 | 0.003 2 | 0.553 7 | 0.112 4 | 0.097 1 | 0.201 8 | 0.186 5 | 0.476 8 | 0.081 7 | 0.000 4 | 0.216 8 | 0.000 1 | 0.000 3 | 0.001 8 | 0.314 8 | 0.000 3 | 0.000 1 | 0.055 6 | 0.000 2 | 0.832 8 | 0.094 2 | 0.659 6 | 0.002 1 | 0.076 4 | 0.310 8 | 0.293 8 | 0.664 8 | 0.000 1 | 0.000 1 | 0.175 8 | 0.634 2 | 0.130 2 | 0.552 8 | 0.686 8 | 0.700 8 | 0.076 3 | 0.110 6 | 0.770 8 | 0.000 1 | 0.000 4 | 0.430 8 | 0.000 5 | 0.319 6 | 0.166 7 | 0.542 8 | 0.327 7 | 0.205 7 | 0.332 7 | 0.052 7 | 0.375 4 | 0.444 8 | 0.000 2 | 0.012 8 | 0.930 8 | 0.203 1 | 0.000 1 | 0.000 4 | 0.046 4 | 0.175 5 | 0.413 7 | 0.592 6 | 0.471 7 | 0.299 6 | 0.152 8 | 0.340 7 | 0.247 8 | 0.000 2 | 0.000 1 | 0.225 6 | 0.058 2 | 0.037 2 | 0.000 3 | 0.207 1 | 0.862 8 | 0.014 5 | 0.548 5 | 0.033 7 | 0.233 7 | 0.816 7 | 0.000 3 | 0.000 1 | 0.542 7 | 0.123 2 | 0.121 1 | 0.019 2 | 0.000 1 | 0.000 3 | 0.463 7 | 0.454 8 | 0.045 8 | 0.128 8 | 0.557 7 | 0.235 5 | 0.441 7 | 0.063 5 | 0.484 8 | 0.000 3 | 0.308 8 | 0.000 1 | 0.000 2 | 0.000 2 | 0.318 8 | 0.000 1 | 0.000 4 | 0.000 1 | 0.545 7 | 0.543 6 | 0.164 8 | 0.734 4 | 0.000 3 | 0.000 1 | 0.215 8 | 0.371 7 | 0.198 5 | 0.743 5 | 0.205 7 | 0.062 7 | 0.000 4 | 0.079 6 | 0.000 1 | 0.683 7 | 0.547 7 | 0.142 4 | 0.000 6 | 0.441 3 | 0.579 8 | 0.000 1 | 0.464 6 | 0.098 4 | 0.041 1 | 0.000 1 | 0.590 7 | 0.000 2 | 0.000 1 | 0.373 4 | 0.494 5 | 0.174 6 | 0.105 7 | 0.001 8 | 0.895 7 | 0.222 7 | 0.537 5 | 0.307 7 | 0.180 4 | 0.625 5 | 0.000 1 | 0.000 6 | 0.591 8 | 0.609 7 | 0.398 6 | 0.000 1 | 0.766 8 | 0.014 8 | 0.638 8 | 0.000 1 | 0.377 5 | 0.004 6 | 0.206 7 | 0.609 8 | 0.465 2 | ||||||||||||||||||||||||||||
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Minkowski 34D | ![]() | 0.253 7 | 0.463 7 | 0.154 8 | 0.102 7 | 0.381 8 | 0.084 3 | 0.134 8 | 0.000 1 | 0.000 1 | 0.000 1 | 0.386 4 | 0.141 8 | 0.279 8 | 0.737 7 | 0.703 7 | 0.014 8 | 0.164 6 | 0.000 3 | 0.663 4 | 0.092 7 | 0.000 4 | 0.224 7 | 0.291 4 | 0.531 3 | 0.056 8 | 0.000 4 | 0.242 7 | 0.000 1 | 0.000 3 | 0.013 6 | 0.331 7 | 0.000 3 | 0.000 1 | 0.035 8 | 0.001 1 | 0.858 6 | 0.059 7 | 0.650 7 | 0.000 3 | 0.056 7 | 0.353 7 | 0.299 6 | 0.670 7 | 0.000 1 | 0.000 1 | 0.284 7 | 0.484 6 | 0.071 5 | 0.594 7 | 0.720 7 | 0.710 6 | 0.027 5 | 0.068 8 | 0.813 5 | 0.000 1 | 0.005 3 | 0.492 6 | 0.164 1 | 0.274 7 | 0.111 8 | 0.571 7 | 0.307 8 | 0.293 5 | 0.307 8 | 0.150 4 | 0.163 8 | 0.531 7 | 0.002 1 | 0.545 1 | 0.932 6 | 0.093 8 | 0.000 1 | 0.000 4 | 0.002 6 | 0.159 6 | 0.368 8 | 0.581 7 | 0.440 8 | 0.228 8 | 0.406 2 | 0.282 8 | 0.294 7 | 0.000 2 | 0.000 1 | 0.189 7 | 0.060 1 | 0.036 3 | 0.000 3 | 0.000 3 | 0.897 5 | 0.000 8 | 0.525 6 | 0.025 8 | 0.205 8 | 0.771 8 | 0.000 3 | 0.000 1 | 0.593 5 | 0.108 5 | 0.044 3 | 0.000 4 | 0.000 1 | 0.000 3 | 0.282 8 | 0.589 6 | 0.094 7 | 0.169 7 | 0.466 8 | 0.227 7 | 0.419 8 | 0.125 3 | 0.757 5 | 0.002 1 | 0.334 7 | 0.000 1 | 0.000 2 | 0.000 2 | 0.357 6 | 0.000 1 | 0.000 4 | 0.000 1 | 0.582 5 | 0.513 8 | 0.337 5 | 0.612 8 | 0.000 3 | 0.000 1 | 0.250 7 | 0.352 8 | 0.136 8 | 0.724 7 | 0.655 2 | 0.280 3 | 0.000 4 | 0.046 8 | 0.000 1 | 0.606 8 | 0.559 6 | 0.159 2 | 0.102 1 | 0.445 2 | 0.655 2 | 0.000 1 | 0.310 8 | 0.117 2 | 0.000 4 | 0.000 1 | 0.581 8 | 0.026 1 | 0.000 1 | 0.265 8 | 0.483 7 | 0.084 8 | 0.097 8 | 0.044 6 | 0.865 8 | 0.142 8 | 0.588 4 | 0.351 6 | 0.272 2 | 0.596 8 | 0.000 1 | 0.003 4 | 0.622 7 | 0.720 4 | 0.096 8 | 0.000 1 | 0.771 7 | 0.016 7 | 0.772 6 | 0.000 1 | 0.302 6 | 0.194 4 | 0.214 6 | 0.621 7 | 0.197 8 | ||||||||||||||||||||||||||||
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 |