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