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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ALS-MinkowskiNet | 0.414 1 | 0.610 1 | 0.322 2 | 0.271 1 | 0.542 1 | 0.153 1 | 0.159 9 | 0.000 1 | 0.000 3 | 0.000 1 | 0.404 3 | 0.503 3 | 0.532 5 | 0.672 13 | 0.804 4 | 0.285 1 | 0.888 1 | 0.000 3 | 0.900 1 | 0.226 1 | 0.087 2 | 0.598 3 | 0.342 3 | 0.671 1 | 0.217 8 | 0.087 2 | 0.449 2 | 0.000 1 | 0.000 3 | 0.253 1 | 0.477 4 | 1.000 1 | 0.000 1 | 0.118 3 | 0.000 2 | 0.905 1 | 0.071 11 | 0.710 1 | 0.076 1 | 0.047 12 | 0.665 1 | 0.376 7 | 0.981 1 | 0.000 1 | 0.000 2 | 0.466 6 | 0.632 5 | 0.113 3 | 0.769 1 | 0.956 2 | 0.795 1 | 0.031 8 | 0.314 1 | 0.936 1 | 0.000 1 | 0.390 2 | 0.601 1 | 0.000 7 | 0.458 7 | 0.366 1 | 0.719 2 | 0.440 4 | 0.564 1 | 0.699 3 | 0.314 1 | 0.464 5 | 0.784 1 | 0.200 1 | 0.283 4 | 0.973 1 | 0.142 8 | 0.000 1 | 0.250 4 | 0.285 4 | 0.220 5 | 0.718 1 | 0.752 4 | 0.723 1 | 0.460 1 | 0.248 12 | 0.475 6 | 0.463 10 | 0.000 3 | 0.000 1 | 0.446 6 | 0.021 4 | 0.025 7 | 0.285 1 | 0.000 3 | 0.972 1 | 0.149 5 | 0.769 1 | 0.230 1 | 0.535 1 | 0.879 2 | 0.252 4 | 0.000 1 | 0.693 1 | 0.129 2 | 0.000 10 | 0.000 4 | 0.000 1 | 0.447 1 | 0.958 1 | 0.662 6 | 0.159 2 | 0.598 2 | 0.780 10 | 0.344 2 | 0.646 2 | 0.106 4 | 0.893 1 | 0.135 1 | 0.455 3 | 0.000 1 | 0.194 3 | 0.259 1 | 0.726 2 | 0.475 1 | 0.000 5 | 0.000 1 | 0.741 1 | 0.865 1 | 0.571 2 | 0.817 3 | 0.445 3 | 0.000 1 | 0.506 1 | 0.630 2 | 0.230 8 | 0.916 1 | 0.728 1 | 0.635 1 | 1.000 1 | 0.252 4 | 0.000 1 | 0.804 1 | 0.697 3 | 0.137 8 | 0.043 5 | 0.717 1 | 0.807 2 | 0.000 1 | 0.510 10 | 0.245 1 | 0.000 5 | 0.000 1 | 0.709 2 | 0.000 2 | 0.000 1 | 0.703 1 | 0.572 2 | 0.646 1 | 0.223 9 | 0.531 2 | 0.984 1 | 0.397 2 | 0.813 1 | 0.798 1 | 0.135 11 | 0.800 1 | 0.000 1 | 0.097 1 | 0.832 1 | 0.752 7 | 0.842 7 | 0.000 1 | 0.852 1 | 0.149 9 | 0.846 8 | 0.000 1 | 0.666 4 | 0.359 2 | 0.252 6 | 0.777 1 | 0.690 2 | |||||||||||||||||||||||||||||
PTv3 ScanNet200 | 0.393 2 | 0.592 2 | 0.330 1 | 0.216 2 | 0.520 2 | 0.109 3 | 0.108 12 | 0.000 1 | 0.337 1 | 0.000 1 | 0.310 10 | 0.394 7 | 0.494 10 | 0.753 8 | 0.848 1 | 0.256 3 | 0.717 4 | 0.000 3 | 0.842 2 | 0.192 3 | 0.065 3 | 0.449 7 | 0.346 2 | 0.546 5 | 0.190 9 | 0.000 6 | 0.384 5 | 0.000 1 | 0.000 3 | 0.218 2 | 0.505 1 | 0.791 2 | 0.000 1 | 0.136 2 | 0.000 2 | 0.903 2 | 0.073 10 | 0.687 5 | 0.000 6 | 0.168 1 | 0.551 4 | 0.387 6 | 0.941 2 | 0.000 1 | 0.000 2 | 0.397 9 | 0.654 3 | 0.000 9 | 0.714 4 | 0.759 11 | 0.752 6 | 0.118 4 | 0.264 3 | 0.926 2 | 0.000 1 | 0.048 4 | 0.575 3 | 0.000 7 | 0.597 1 | 0.366 1 | 0.755 1 | 0.469 1 | 0.474 2 | 0.798 1 | 0.140 7 | 0.617 1 | 0.692 5 | 0.000 5 | 0.592 2 | 0.971 2 | 0.188 3 | 0.000 1 | 0.133 6 | 0.593 1 | 0.349 1 | 0.650 3 | 0.717 6 | 0.699 2 | 0.455 2 | 0.790 1 | 0.523 3 | 0.636 1 | 0.301 1 | 0.000 1 | 0.622 2 | 0.000 8 | 0.017 11 | 0.259 3 | 0.000 3 | 0.921 3 | 0.337 1 | 0.733 2 | 0.210 2 | 0.514 2 | 0.860 7 | 0.407 1 | 0.000 1 | 0.688 2 | 0.109 7 | 0.000 10 | 0.000 4 | 0.000 1 | 0.151 3 | 0.671 5 | 0.782 1 | 0.115 9 | 0.641 1 | 0.903 1 | 0.349 1 | 0.616 3 | 0.088 5 | 0.832 4 | 0.000 4 | 0.480 2 | 0.000 1 | 0.428 1 | 0.000 3 | 0.497 8 | 0.000 2 | 0.000 5 | 0.000 1 | 0.662 3 | 0.690 2 | 0.612 1 | 0.828 1 | 0.575 1 | 0.000 1 | 0.404 5 | 0.644 1 | 0.325 4 | 0.887 3 | 0.728 1 | 0.009 12 | 0.134 6 | 0.026 13 | 0.000 1 | 0.761 2 | 0.731 1 | 0.172 4 | 0.077 2 | 0.528 4 | 0.727 4 | 0.000 1 | 0.603 4 | 0.220 3 | 0.022 2 | 0.000 1 | 0.740 1 | 0.000 2 | 0.000 1 | 0.661 3 | 0.586 1 | 0.566 2 | 0.436 4 | 0.531 2 | 0.978 2 | 0.457 1 | 0.708 3 | 0.583 4 | 0.141 7 | 0.748 2 | 0.000 1 | 0.026 2 | 0.822 2 | 0.871 3 | 0.879 5 | 0.000 1 | 0.851 2 | 0.405 2 | 0.914 1 | 0.000 1 | 0.682 2 | 0.000 11 | 0.281 2 | 0.738 2 | 0.463 6 | |||||||||||||||||||||||||||||
Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao: Point Transformer V3: Simpler, Faster, Stronger. CVPR 2024 (Oral) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CeCo | 0.340 5 | 0.551 7 | 0.247 9 | 0.181 4 | 0.475 9 | 0.057 13 | 0.142 10 | 0.000 1 | 0.000 3 | 0.000 1 | 0.387 4 | 0.463 4 | 0.499 8 | 0.924 2 | 0.774 8 | 0.213 5 | 0.257 9 | 0.000 3 | 0.546 11 | 0.100 9 | 0.006 6 | 0.615 1 | 0.177 13 | 0.534 6 | 0.246 4 | 0.000 6 | 0.400 3 | 0.000 1 | 0.338 1 | 0.006 12 | 0.484 3 | 0.609 3 | 0.000 1 | 0.083 9 | 0.000 2 | 0.873 8 | 0.089 5 | 0.661 10 | 0.000 6 | 0.048 11 | 0.560 3 | 0.408 5 | 0.892 6 | 0.000 1 | 0.000 2 | 0.586 1 | 0.616 6 | 0.000 9 | 0.692 7 | 0.900 4 | 0.721 8 | 0.162 1 | 0.228 4 | 0.860 7 | 0.000 1 | 0.000 9 | 0.575 3 | 0.083 3 | 0.550 3 | 0.347 3 | 0.624 9 | 0.410 9 | 0.360 5 | 0.740 2 | 0.109 9 | 0.321 11 | 0.660 6 | 0.000 5 | 0.121 5 | 0.939 9 | 0.143 7 | 0.000 1 | 0.400 1 | 0.003 9 | 0.190 8 | 0.564 4 | 0.652 8 | 0.615 7 | 0.421 3 | 0.304 10 | 0.579 1 | 0.547 4 | 0.000 3 | 0.000 1 | 0.296 10 | 0.000 8 | 0.030 6 | 0.096 5 | 0.000 3 | 0.916 4 | 0.037 9 | 0.551 8 | 0.171 5 | 0.376 6 | 0.865 6 | 0.286 3 | 0.000 1 | 0.633 3 | 0.102 10 | 0.027 6 | 0.011 3 | 0.000 1 | 0.000 7 | 0.474 10 | 0.742 2 | 0.133 6 | 0.311 9 | 0.824 7 | 0.242 9 | 0.503 10 | 0.068 7 | 0.828 5 | 0.000 4 | 0.429 5 | 0.000 1 | 0.063 4 | 0.000 3 | 0.781 1 | 0.000 2 | 0.000 5 | 0.000 1 | 0.665 2 | 0.633 5 | 0.450 5 | 0.818 2 | 0.000 8 | 0.000 1 | 0.429 3 | 0.532 6 | 0.226 9 | 0.825 7 | 0.510 9 | 0.377 4 | 0.709 2 | 0.079 10 | 0.000 1 | 0.753 4 | 0.683 4 | 0.102 12 | 0.063 3 | 0.401 12 | 0.620 10 | 0.000 1 | 0.619 2 | 0.000 12 | 0.000 5 | 0.000 1 | 0.595 11 | 0.000 2 | 0.000 1 | 0.345 10 | 0.564 4 | 0.411 6 | 0.603 1 | 0.384 5 | 0.945 6 | 0.266 7 | 0.643 5 | 0.367 10 | 0.304 1 | 0.663 8 | 0.000 1 | 0.010 4 | 0.726 11 | 0.767 6 | 0.898 3 | 0.000 1 | 0.784 9 | 0.435 1 | 0.861 5 | 0.000 1 | 0.447 8 | 0.000 11 | 0.257 5 | 0.656 9 | 0.377 9 | |||||||||||||||||||||||||||||
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia: Understanding Imbalanced Semantic Segmentation Through Neural Collapse. CVPR 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
BFANet ScanNet200 | 0.360 3 | 0.553 5 | 0.293 3 | 0.193 3 | 0.483 7 | 0.096 4 | 0.266 4 | 0.000 1 | 0.000 3 | 0.000 1 | 0.298 11 | 0.255 10 | 0.661 1 | 0.810 5 | 0.810 2 | 0.194 7 | 0.785 3 | 0.000 3 | 0.000 13 | 0.161 4 | 0.000 7 | 0.494 6 | 0.382 1 | 0.574 3 | 0.258 3 | 0.000 6 | 0.372 7 | 0.000 1 | 0.000 3 | 0.043 10 | 0.436 6 | 0.000 7 | 0.000 1 | 0.239 1 | 0.000 2 | 0.901 3 | 0.105 1 | 0.689 3 | 0.025 3 | 0.128 2 | 0.614 2 | 0.436 1 | 0.493 13 | 0.000 1 | 0.000 2 | 0.526 4 | 0.546 9 | 0.109 4 | 0.651 11 | 0.953 3 | 0.753 5 | 0.101 5 | 0.143 9 | 0.897 3 | 0.000 1 | 0.431 1 | 0.469 11 | 0.000 7 | 0.522 5 | 0.337 4 | 0.661 5 | 0.459 2 | 0.409 3 | 0.666 4 | 0.102 10 | 0.508 4 | 0.757 3 | 0.000 5 | 0.060 10 | 0.970 3 | 0.497 1 | 0.000 1 | 0.376 2 | 0.511 2 | 0.262 4 | 0.688 2 | 0.921 1 | 0.617 6 | 0.321 10 | 0.590 3 | 0.491 5 | 0.556 3 | 0.000 3 | 0.000 1 | 0.481 3 | 0.093 1 | 0.043 2 | 0.284 2 | 0.000 3 | 0.875 11 | 0.135 6 | 0.669 4 | 0.124 9 | 0.394 5 | 0.849 10 | 0.298 2 | 0.000 1 | 0.476 13 | 0.088 11 | 0.042 5 | 0.000 4 | 0.000 1 | 0.254 2 | 0.653 7 | 0.741 3 | 0.215 1 | 0.573 4 | 0.852 4 | 0.266 6 | 0.654 1 | 0.056 10 | 0.835 2 | 0.000 4 | 0.492 1 | 0.000 1 | 0.000 5 | 0.000 3 | 0.612 7 | 0.000 2 | 0.000 5 | 0.000 1 | 0.616 4 | 0.469 13 | 0.460 4 | 0.698 10 | 0.516 2 | 0.000 1 | 0.378 6 | 0.563 3 | 0.476 2 | 0.863 4 | 0.574 7 | 0.330 5 | 0.000 8 | 0.282 3 | 0.000 1 | 0.760 3 | 0.710 2 | 0.233 1 | 0.000 8 | 0.641 3 | 0.814 1 | 0.000 1 | 0.585 7 | 0.053 9 | 0.000 5 | 0.000 1 | 0.629 8 | 0.000 2 | 0.000 1 | 0.678 2 | 0.528 9 | 0.534 3 | 0.129 10 | 0.596 1 | 0.973 3 | 0.264 8 | 0.772 2 | 0.526 6 | 0.139 9 | 0.707 3 | 0.000 1 | 0.000 9 | 0.764 10 | 0.591 12 | 0.848 6 | 0.000 1 | 0.827 3 | 0.338 3 | 0.806 10 | 0.000 1 | 0.568 6 | 0.151 6 | 0.358 1 | 0.659 8 | 0.510 4 | |||||||||||||||||||||||||||||
PonderV2 ScanNet200 | 0.346 4 | 0.552 6 | 0.270 6 | 0.175 5 | 0.497 6 | 0.070 10 | 0.239 5 | 0.000 1 | 0.000 3 | 0.000 1 | 0.232 13 | 0.412 6 | 0.584 2 | 0.842 3 | 0.804 4 | 0.212 6 | 0.540 6 | 0.000 3 | 0.433 12 | 0.106 8 | 0.000 7 | 0.590 4 | 0.290 8 | 0.548 4 | 0.243 5 | 0.000 6 | 0.356 9 | 0.000 1 | 0.000 3 | 0.062 8 | 0.398 9 | 0.441 6 | 0.000 1 | 0.104 8 | 0.000 2 | 0.888 4 | 0.076 9 | 0.682 6 | 0.030 2 | 0.094 5 | 0.491 8 | 0.351 9 | 0.869 8 | 0.000 1 | 0.063 1 | 0.403 8 | 0.700 2 | 0.000 9 | 0.660 10 | 0.881 5 | 0.761 2 | 0.050 7 | 0.186 6 | 0.852 9 | 0.000 1 | 0.007 7 | 0.570 6 | 0.100 2 | 0.565 2 | 0.326 5 | 0.641 8 | 0.431 5 | 0.290 10 | 0.621 5 | 0.259 3 | 0.408 7 | 0.622 8 | 0.125 2 | 0.082 8 | 0.950 4 | 0.179 4 | 0.000 1 | 0.263 3 | 0.424 3 | 0.193 7 | 0.558 5 | 0.880 2 | 0.545 9 | 0.375 5 | 0.727 2 | 0.445 8 | 0.499 7 | 0.000 3 | 0.000 1 | 0.475 5 | 0.002 6 | 0.034 5 | 0.083 6 | 0.000 3 | 0.924 2 | 0.290 3 | 0.636 5 | 0.115 10 | 0.400 4 | 0.874 4 | 0.186 6 | 0.000 1 | 0.611 6 | 0.128 3 | 0.113 2 | 0.000 4 | 0.000 1 | 0.000 7 | 0.584 8 | 0.636 7 | 0.103 10 | 0.385 7 | 0.843 5 | 0.283 3 | 0.603 5 | 0.080 6 | 0.825 6 | 0.000 4 | 0.377 8 | 0.000 1 | 0.000 5 | 0.000 3 | 0.457 9 | 0.000 2 | 0.000 5 | 0.000 1 | 0.574 10 | 0.608 7 | 0.481 3 | 0.792 4 | 0.394 4 | 0.000 1 | 0.357 8 | 0.503 9 | 0.261 7 | 0.817 9 | 0.504 10 | 0.304 6 | 0.472 4 | 0.115 7 | 0.000 1 | 0.750 5 | 0.677 5 | 0.202 2 | 0.000 8 | 0.509 5 | 0.729 3 | 0.000 1 | 0.519 9 | 0.000 12 | 0.000 5 | 0.000 1 | 0.620 10 | 0.000 2 | 0.000 1 | 0.660 5 | 0.560 5 | 0.486 4 | 0.384 6 | 0.346 6 | 0.952 4 | 0.247 10 | 0.667 4 | 0.436 8 | 0.269 3 | 0.691 5 | 0.000 1 | 0.010 4 | 0.787 6 | 0.889 2 | 0.880 4 | 0.000 1 | 0.810 6 | 0.336 4 | 0.860 6 | 0.000 1 | 0.606 5 | 0.009 7 | 0.248 7 | 0.681 5 | 0.392 8 | |||||||||||||||||||||||||||||
Haoyi Zhu, Honghui Yang, Xiaoyang Wu, Di Huang, Sha Zhang, Xianglong He, Tong He, Hengshuang Zhao, Chunhua Shen, Yu Qiao, Wanli Ouyang: PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
OA-CNN-L_ScanNet200 | 0.333 7 | 0.558 3 | 0.269 7 | 0.124 9 | 0.448 11 | 0.080 7 | 0.272 3 | 0.000 1 | 0.000 3 | 0.000 1 | 0.342 6 | 0.515 2 | 0.524 6 | 0.713 12 | 0.789 6 | 0.158 9 | 0.384 8 | 0.000 3 | 0.806 4 | 0.125 5 | 0.000 7 | 0.496 5 | 0.332 5 | 0.498 12 | 0.227 6 | 0.024 3 | 0.474 1 | 0.000 1 | 0.003 2 | 0.071 7 | 0.487 2 | 0.000 7 | 0.000 1 | 0.110 6 | 0.000 2 | 0.876 6 | 0.013 13 | 0.703 2 | 0.000 6 | 0.076 7 | 0.473 9 | 0.355 8 | 0.906 5 | 0.000 1 | 0.000 2 | 0.476 5 | 0.706 1 | 0.000 9 | 0.672 9 | 0.835 9 | 0.748 7 | 0.015 12 | 0.223 5 | 0.860 7 | 0.000 1 | 0.000 9 | 0.572 5 | 0.000 7 | 0.509 6 | 0.313 6 | 0.662 3 | 0.398 10 | 0.396 4 | 0.411 11 | 0.276 2 | 0.527 2 | 0.711 4 | 0.000 5 | 0.076 9 | 0.946 5 | 0.166 5 | 0.000 1 | 0.022 7 | 0.160 5 | 0.183 9 | 0.493 9 | 0.699 7 | 0.637 4 | 0.403 4 | 0.330 9 | 0.406 9 | 0.526 5 | 0.024 2 | 0.000 1 | 0.392 9 | 0.000 8 | 0.016 12 | 0.000 8 | 0.196 2 | 0.915 5 | 0.112 8 | 0.557 7 | 0.197 3 | 0.352 8 | 0.877 3 | 0.000 8 | 0.000 1 | 0.592 10 | 0.103 9 | 0.000 10 | 0.067 1 | 0.000 1 | 0.089 4 | 0.735 4 | 0.625 8 | 0.130 8 | 0.568 5 | 0.836 6 | 0.271 4 | 0.534 7 | 0.043 11 | 0.799 7 | 0.001 3 | 0.445 4 | 0.000 1 | 0.000 5 | 0.024 2 | 0.661 3 | 0.000 2 | 0.262 2 | 0.000 1 | 0.591 6 | 0.517 11 | 0.373 7 | 0.788 6 | 0.021 7 | 0.000 1 | 0.455 2 | 0.517 7 | 0.320 5 | 0.823 8 | 0.200 13 | 0.001 13 | 0.150 5 | 0.100 8 | 0.000 1 | 0.736 7 | 0.668 6 | 0.103 11 | 0.052 4 | 0.662 2 | 0.720 5 | 0.000 1 | 0.602 5 | 0.112 5 | 0.002 4 | 0.000 1 | 0.637 7 | 0.000 2 | 0.000 1 | 0.621 8 | 0.569 3 | 0.398 7 | 0.412 5 | 0.234 8 | 0.949 5 | 0.363 3 | 0.492 11 | 0.495 7 | 0.251 4 | 0.665 7 | 0.000 1 | 0.001 8 | 0.805 4 | 0.833 5 | 0.794 8 | 0.000 1 | 0.821 4 | 0.314 5 | 0.843 9 | 0.000 1 | 0.560 7 | 0.245 3 | 0.262 4 | 0.713 3 | 0.370 10 | |||||||||||||||||||||||||||||
PPT-SpUNet-F.T. | 0.332 8 | 0.556 4 | 0.270 5 | 0.123 10 | 0.519 3 | 0.091 5 | 0.349 2 | 0.000 1 | 0.000 3 | 0.000 1 | 0.339 7 | 0.383 8 | 0.498 9 | 0.833 4 | 0.807 3 | 0.241 4 | 0.584 5 | 0.000 3 | 0.755 5 | 0.124 6 | 0.000 7 | 0.608 2 | 0.330 6 | 0.530 8 | 0.314 1 | 0.000 6 | 0.374 6 | 0.000 1 | 0.000 3 | 0.197 3 | 0.459 5 | 0.000 7 | 0.000 1 | 0.117 4 | 0.000 2 | 0.876 6 | 0.095 2 | 0.682 6 | 0.000 6 | 0.086 6 | 0.518 6 | 0.433 2 | 0.930 3 | 0.000 1 | 0.000 2 | 0.563 3 | 0.542 10 | 0.077 6 | 0.715 3 | 0.858 7 | 0.756 4 | 0.008 13 | 0.171 8 | 0.874 6 | 0.000 1 | 0.039 5 | 0.550 7 | 0.000 7 | 0.545 4 | 0.256 7 | 0.657 7 | 0.453 3 | 0.351 6 | 0.449 9 | 0.213 4 | 0.392 8 | 0.611 9 | 0.000 5 | 0.037 11 | 0.946 5 | 0.138 10 | 0.000 1 | 0.000 9 | 0.063 7 | 0.308 2 | 0.537 6 | 0.796 3 | 0.673 3 | 0.323 9 | 0.392 7 | 0.400 10 | 0.509 6 | 0.000 3 | 0.000 1 | 0.649 1 | 0.000 8 | 0.023 8 | 0.000 8 | 0.000 3 | 0.914 6 | 0.002 12 | 0.506 12 | 0.163 7 | 0.359 7 | 0.872 5 | 0.000 8 | 0.000 1 | 0.623 5 | 0.112 5 | 0.001 9 | 0.000 4 | 0.000 1 | 0.021 5 | 0.753 2 | 0.565 12 | 0.150 3 | 0.579 3 | 0.806 8 | 0.267 5 | 0.616 3 | 0.042 12 | 0.783 9 | 0.000 4 | 0.374 9 | 0.000 1 | 0.000 5 | 0.000 3 | 0.620 6 | 0.000 2 | 0.000 5 | 0.000 1 | 0.572 11 | 0.634 4 | 0.350 8 | 0.792 4 | 0.000 8 | 0.000 1 | 0.376 7 | 0.535 5 | 0.378 3 | 0.855 5 | 0.672 3 | 0.074 9 | 0.000 8 | 0.185 6 | 0.000 1 | 0.727 8 | 0.660 8 | 0.076 13 | 0.000 8 | 0.432 8 | 0.646 7 | 0.000 1 | 0.594 6 | 0.006 11 | 0.000 5 | 0.000 1 | 0.658 5 | 0.000 2 | 0.000 1 | 0.661 3 | 0.549 6 | 0.300 10 | 0.291 8 | 0.045 10 | 0.942 8 | 0.304 5 | 0.600 7 | 0.572 5 | 0.135 11 | 0.695 4 | 0.000 1 | 0.008 6 | 0.793 5 | 0.942 1 | 0.899 2 | 0.000 1 | 0.816 5 | 0.181 7 | 0.897 2 | 0.000 1 | 0.679 3 | 0.223 4 | 0.264 3 | 0.691 4 | 0.345 11 | |||||||||||||||||||||||||||||
Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao: Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training. CVPR 2024 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
AWCS | 0.305 10 | 0.508 10 | 0.225 10 | 0.142 7 | 0.463 10 | 0.063 11 | 0.195 7 | 0.000 1 | 0.000 3 | 0.000 1 | 0.467 2 | 0.551 1 | 0.504 7 | 0.773 6 | 0.764 10 | 0.142 10 | 0.029 13 | 0.000 3 | 0.626 9 | 0.100 9 | 0.000 7 | 0.360 10 | 0.179 11 | 0.507 11 | 0.137 11 | 0.006 5 | 0.300 10 | 0.000 1 | 0.000 3 | 0.172 6 | 0.364 11 | 0.512 5 | 0.000 1 | 0.056 10 | 0.000 2 | 0.865 10 | 0.093 4 | 0.634 13 | 0.000 6 | 0.071 9 | 0.396 11 | 0.296 12 | 0.876 7 | 0.000 1 | 0.000 2 | 0.373 10 | 0.436 12 | 0.063 8 | 0.749 2 | 0.877 6 | 0.721 8 | 0.131 3 | 0.124 10 | 0.804 11 | 0.000 1 | 0.000 9 | 0.515 8 | 0.010 6 | 0.452 9 | 0.252 8 | 0.578 10 | 0.417 7 | 0.179 13 | 0.484 8 | 0.171 5 | 0.337 10 | 0.606 10 | 0.000 5 | 0.115 6 | 0.937 10 | 0.142 8 | 0.000 1 | 0.008 8 | 0.000 11 | 0.157 12 | 0.484 10 | 0.402 13 | 0.501 11 | 0.339 7 | 0.553 4 | 0.529 2 | 0.478 9 | 0.000 3 | 0.000 1 | 0.404 8 | 0.001 7 | 0.022 9 | 0.077 7 | 0.000 3 | 0.894 9 | 0.219 4 | 0.628 6 | 0.093 11 | 0.305 10 | 0.886 1 | 0.233 5 | 0.000 1 | 0.603 7 | 0.112 5 | 0.023 7 | 0.000 4 | 0.000 1 | 0.000 7 | 0.741 3 | 0.664 5 | 0.097 11 | 0.253 10 | 0.782 9 | 0.264 7 | 0.523 9 | 0.154 1 | 0.707 12 | 0.000 4 | 0.411 6 | 0.000 1 | 0.000 5 | 0.000 3 | 0.332 12 | 0.000 2 | 0.000 5 | 0.000 1 | 0.602 5 | 0.595 8 | 0.185 11 | 0.656 12 | 0.159 5 | 0.000 1 | 0.355 9 | 0.424 11 | 0.154 11 | 0.729 11 | 0.516 8 | 0.220 8 | 0.620 3 | 0.084 9 | 0.000 1 | 0.707 10 | 0.651 9 | 0.173 3 | 0.014 7 | 0.381 13 | 0.582 11 | 0.000 1 | 0.619 2 | 0.049 10 | 0.000 5 | 0.000 1 | 0.702 3 | 0.000 2 | 0.000 1 | 0.302 12 | 0.489 11 | 0.317 9 | 0.334 7 | 0.392 4 | 0.922 10 | 0.254 9 | 0.533 10 | 0.394 9 | 0.129 13 | 0.613 11 | 0.000 1 | 0.000 9 | 0.820 3 | 0.649 9 | 0.749 10 | 0.000 1 | 0.782 10 | 0.282 6 | 0.863 4 | 0.000 1 | 0.288 12 | 0.006 8 | 0.220 9 | 0.633 10 | 0.542 3 | |||||||||||||||||||||||||||||
LGround | 0.272 11 | 0.485 11 | 0.184 11 | 0.106 11 | 0.476 8 | 0.077 8 | 0.218 6 | 0.000 1 | 0.000 3 | 0.000 1 | 0.547 1 | 0.295 9 | 0.540 4 | 0.746 9 | 0.745 11 | 0.058 12 | 0.112 12 | 0.005 1 | 0.658 7 | 0.077 13 | 0.000 7 | 0.322 11 | 0.178 12 | 0.512 10 | 0.190 9 | 0.199 1 | 0.277 11 | 0.000 1 | 0.000 3 | 0.173 5 | 0.399 8 | 0.000 7 | 0.000 1 | 0.039 12 | 0.000 2 | 0.858 11 | 0.085 6 | 0.676 8 | 0.002 4 | 0.103 4 | 0.498 7 | 0.323 10 | 0.703 10 | 0.000 1 | 0.000 2 | 0.296 11 | 0.549 8 | 0.216 1 | 0.702 5 | 0.768 10 | 0.718 10 | 0.028 9 | 0.092 12 | 0.786 12 | 0.000 1 | 0.000 9 | 0.453 12 | 0.022 5 | 0.251 13 | 0.252 8 | 0.572 11 | 0.348 11 | 0.321 7 | 0.514 6 | 0.063 11 | 0.279 12 | 0.552 11 | 0.000 5 | 0.019 12 | 0.932 11 | 0.132 12 | 0.000 1 | 0.000 9 | 0.000 11 | 0.156 13 | 0.457 11 | 0.623 9 | 0.518 10 | 0.265 12 | 0.358 8 | 0.381 11 | 0.395 11 | 0.000 3 | 0.000 1 | 0.127 13 | 0.012 5 | 0.051 1 | 0.000 8 | 0.000 3 | 0.886 10 | 0.014 10 | 0.437 13 | 0.179 4 | 0.244 11 | 0.826 11 | 0.000 8 | 0.000 1 | 0.599 8 | 0.136 1 | 0.085 3 | 0.000 4 | 0.000 1 | 0.000 7 | 0.565 9 | 0.612 10 | 0.143 4 | 0.207 11 | 0.566 11 | 0.232 11 | 0.446 11 | 0.127 2 | 0.708 11 | 0.000 4 | 0.384 7 | 0.000 1 | 0.000 5 | 0.000 3 | 0.402 10 | 0.000 2 | 0.059 3 | 0.000 1 | 0.525 13 | 0.566 9 | 0.229 10 | 0.659 11 | 0.000 8 | 0.000 1 | 0.265 11 | 0.446 10 | 0.147 12 | 0.720 13 | 0.597 6 | 0.066 10 | 0.000 8 | 0.187 5 | 0.000 1 | 0.726 9 | 0.467 13 | 0.134 9 | 0.000 8 | 0.413 11 | 0.629 9 | 0.000 1 | 0.363 12 | 0.055 8 | 0.022 2 | 0.000 1 | 0.626 9 | 0.000 2 | 0.000 1 | 0.323 11 | 0.479 13 | 0.154 12 | 0.117 11 | 0.028 12 | 0.901 11 | 0.243 11 | 0.415 12 | 0.295 13 | 0.143 6 | 0.610 12 | 0.000 1 | 0.000 9 | 0.777 8 | 0.397 13 | 0.324 12 | 0.000 1 | 0.778 11 | 0.179 8 | 0.702 12 | 0.000 1 | 0.274 13 | 0.404 1 | 0.233 8 | 0.622 11 | 0.398 7 | |||||||||||||||||||||||||||||
David Rozenberszki, Or Litany, Angela Dai: Language-Grounded Indoor 3D Semantic Segmentation in the Wild. arXiv | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
OctFormer ScanNet200 | 0.326 9 | 0.539 8 | 0.265 8 | 0.131 8 | 0.499 5 | 0.110 2 | 0.522 1 | 0.000 1 | 0.000 3 | 0.000 1 | 0.318 9 | 0.427 5 | 0.455 11 | 0.743 10 | 0.765 9 | 0.175 8 | 0.842 2 | 0.000 3 | 0.828 3 | 0.204 2 | 0.033 4 | 0.429 8 | 0.335 4 | 0.601 2 | 0.312 2 | 0.000 6 | 0.357 8 | 0.000 1 | 0.000 3 | 0.047 9 | 0.423 7 | 0.000 7 | 0.000 1 | 0.105 7 | 0.000 2 | 0.873 8 | 0.079 8 | 0.670 9 | 0.000 6 | 0.117 3 | 0.471 10 | 0.432 3 | 0.829 9 | 0.000 1 | 0.000 2 | 0.584 2 | 0.417 13 | 0.089 5 | 0.684 8 | 0.837 8 | 0.705 12 | 0.021 11 | 0.178 7 | 0.892 4 | 0.000 1 | 0.028 6 | 0.505 9 | 0.000 7 | 0.457 8 | 0.200 10 | 0.662 3 | 0.412 8 | 0.244 11 | 0.496 7 | 0.000 13 | 0.451 6 | 0.626 7 | 0.000 5 | 0.102 7 | 0.943 8 | 0.138 10 | 0.000 1 | 0.000 9 | 0.149 6 | 0.291 3 | 0.534 7 | 0.722 5 | 0.632 5 | 0.331 8 | 0.253 11 | 0.453 7 | 0.487 8 | 0.000 3 | 0.000 1 | 0.479 4 | 0.000 8 | 0.022 9 | 0.000 8 | 0.000 3 | 0.900 7 | 0.128 7 | 0.684 3 | 0.164 6 | 0.413 3 | 0.854 9 | 0.000 8 | 0.000 1 | 0.512 12 | 0.074 13 | 0.003 8 | 0.000 4 | 0.000 1 | 0.000 7 | 0.469 11 | 0.613 9 | 0.132 7 | 0.529 6 | 0.871 2 | 0.227 12 | 0.582 6 | 0.026 13 | 0.787 8 | 0.000 4 | 0.339 11 | 0.000 1 | 0.000 5 | 0.000 3 | 0.626 5 | 0.000 2 | 0.029 4 | 0.000 1 | 0.587 7 | 0.612 6 | 0.411 6 | 0.724 8 | 0.000 8 | 0.000 1 | 0.407 4 | 0.552 4 | 0.513 1 | 0.849 6 | 0.655 4 | 0.408 3 | 0.000 8 | 0.296 2 | 0.000 1 | 0.686 11 | 0.645 10 | 0.145 6 | 0.022 6 | 0.414 10 | 0.633 8 | 0.000 1 | 0.637 1 | 0.224 2 | 0.000 5 | 0.000 1 | 0.650 6 | 0.000 2 | 0.000 1 | 0.622 7 | 0.535 8 | 0.343 8 | 0.483 3 | 0.230 9 | 0.943 7 | 0.289 6 | 0.618 6 | 0.596 3 | 0.140 8 | 0.679 6 | 0.000 1 | 0.022 3 | 0.783 7 | 0.620 10 | 0.906 1 | 0.000 1 | 0.806 7 | 0.137 10 | 0.865 3 | 0.000 1 | 0.378 9 | 0.000 11 | 0.168 13 | 0.680 6 | 0.227 12 | |||||||||||||||||||||||||||||
Peng-Shuai Wang: OctFormer: Octree-based Transformers for 3D Point Clouds. SIGGRAPH 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
CSC-Pretrain | 0.249 13 | 0.455 13 | 0.171 12 | 0.079 13 | 0.418 12 | 0.059 12 | 0.186 8 | 0.000 1 | 0.000 3 | 0.000 1 | 0.335 8 | 0.250 11 | 0.316 12 | 0.766 7 | 0.697 13 | 0.142 10 | 0.170 10 | 0.003 2 | 0.553 10 | 0.112 7 | 0.097 1 | 0.201 13 | 0.186 10 | 0.476 13 | 0.081 12 | 0.000 6 | 0.216 13 | 0.000 1 | 0.000 3 | 0.001 13 | 0.314 13 | 0.000 7 | 0.000 1 | 0.055 11 | 0.000 2 | 0.832 13 | 0.094 3 | 0.659 11 | 0.002 4 | 0.076 7 | 0.310 13 | 0.293 13 | 0.664 12 | 0.000 1 | 0.000 2 | 0.175 13 | 0.634 4 | 0.130 2 | 0.552 13 | 0.686 13 | 0.700 13 | 0.076 6 | 0.110 11 | 0.770 13 | 0.000 1 | 0.000 9 | 0.430 13 | 0.000 7 | 0.319 11 | 0.166 11 | 0.542 13 | 0.327 12 | 0.205 12 | 0.332 12 | 0.052 12 | 0.375 9 | 0.444 13 | 0.000 5 | 0.012 13 | 0.930 13 | 0.203 2 | 0.000 1 | 0.000 9 | 0.046 8 | 0.175 10 | 0.413 12 | 0.592 10 | 0.471 12 | 0.299 11 | 0.152 13 | 0.340 12 | 0.247 13 | 0.000 3 | 0.000 1 | 0.225 11 | 0.058 3 | 0.037 3 | 0.000 8 | 0.207 1 | 0.862 12 | 0.014 10 | 0.548 9 | 0.033 12 | 0.233 12 | 0.816 12 | 0.000 8 | 0.000 1 | 0.542 11 | 0.123 4 | 0.121 1 | 0.019 2 | 0.000 1 | 0.000 7 | 0.463 12 | 0.454 13 | 0.045 13 | 0.128 13 | 0.557 12 | 0.235 10 | 0.441 12 | 0.063 9 | 0.484 13 | 0.000 4 | 0.308 13 | 0.000 1 | 0.000 5 | 0.000 3 | 0.318 13 | 0.000 2 | 0.000 5 | 0.000 1 | 0.545 12 | 0.543 10 | 0.164 12 | 0.734 7 | 0.000 8 | 0.000 1 | 0.215 13 | 0.371 12 | 0.198 10 | 0.743 10 | 0.205 12 | 0.062 11 | 0.000 8 | 0.079 10 | 0.000 1 | 0.683 12 | 0.547 12 | 0.142 7 | 0.000 8 | 0.441 7 | 0.579 12 | 0.000 1 | 0.464 11 | 0.098 7 | 0.041 1 | 0.000 1 | 0.590 12 | 0.000 2 | 0.000 1 | 0.373 9 | 0.494 10 | 0.174 11 | 0.105 12 | 0.001 13 | 0.895 12 | 0.222 12 | 0.537 9 | 0.307 12 | 0.180 5 | 0.625 10 | 0.000 1 | 0.000 9 | 0.591 13 | 0.609 11 | 0.398 11 | 0.000 1 | 0.766 13 | 0.014 13 | 0.638 13 | 0.000 1 | 0.377 10 | 0.004 9 | 0.206 11 | 0.609 13 | 0.465 5 | |||||||||||||||||||||||||||||
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie: Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts. CVPR 2021 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Minkowski 34D | 0.253 12 | 0.463 12 | 0.154 13 | 0.102 12 | 0.381 13 | 0.084 6 | 0.134 11 | 0.000 1 | 0.000 3 | 0.000 1 | 0.386 5 | 0.141 13 | 0.279 13 | 0.737 11 | 0.703 12 | 0.014 13 | 0.164 11 | 0.000 3 | 0.663 6 | 0.092 12 | 0.000 7 | 0.224 12 | 0.291 7 | 0.531 7 | 0.056 13 | 0.000 6 | 0.242 12 | 0.000 1 | 0.000 3 | 0.013 11 | 0.331 12 | 0.000 7 | 0.000 1 | 0.035 13 | 0.001 1 | 0.858 11 | 0.059 12 | 0.650 12 | 0.000 6 | 0.056 10 | 0.353 12 | 0.299 11 | 0.670 11 | 0.000 1 | 0.000 2 | 0.284 12 | 0.484 11 | 0.071 7 | 0.594 12 | 0.720 12 | 0.710 11 | 0.027 10 | 0.068 13 | 0.813 10 | 0.000 1 | 0.005 8 | 0.492 10 | 0.164 1 | 0.274 12 | 0.111 12 | 0.571 12 | 0.307 13 | 0.293 9 | 0.307 13 | 0.150 6 | 0.163 13 | 0.531 12 | 0.002 4 | 0.545 3 | 0.932 11 | 0.093 13 | 0.000 1 | 0.000 9 | 0.002 10 | 0.159 11 | 0.368 13 | 0.581 11 | 0.440 13 | 0.228 13 | 0.406 6 | 0.282 13 | 0.294 12 | 0.000 3 | 0.000 1 | 0.189 12 | 0.060 2 | 0.036 4 | 0.000 8 | 0.000 3 | 0.897 8 | 0.000 13 | 0.525 10 | 0.025 13 | 0.205 13 | 0.771 13 | 0.000 8 | 0.000 1 | 0.593 9 | 0.108 8 | 0.044 4 | 0.000 4 | 0.000 1 | 0.000 7 | 0.282 13 | 0.589 11 | 0.094 12 | 0.169 12 | 0.466 13 | 0.227 12 | 0.419 13 | 0.125 3 | 0.757 10 | 0.002 2 | 0.334 12 | 0.000 1 | 0.000 5 | 0.000 3 | 0.357 11 | 0.000 2 | 0.000 5 | 0.000 1 | 0.582 8 | 0.513 12 | 0.337 9 | 0.612 13 | 0.000 8 | 0.000 1 | 0.250 12 | 0.352 13 | 0.136 13 | 0.724 12 | 0.655 4 | 0.280 7 | 0.000 8 | 0.046 12 | 0.000 1 | 0.606 13 | 0.559 11 | 0.159 5 | 0.102 1 | 0.445 6 | 0.655 6 | 0.000 1 | 0.310 13 | 0.117 4 | 0.000 5 | 0.000 1 | 0.581 13 | 0.026 1 | 0.000 1 | 0.265 13 | 0.483 12 | 0.084 13 | 0.097 13 | 0.044 11 | 0.865 13 | 0.142 13 | 0.588 8 | 0.351 11 | 0.272 2 | 0.596 13 | 0.000 1 | 0.003 7 | 0.622 12 | 0.720 8 | 0.096 13 | 0.000 1 | 0.771 12 | 0.016 12 | 0.772 11 | 0.000 1 | 0.302 11 | 0.194 5 | 0.214 10 | 0.621 12 | 0.197 13 | |||||||||||||||||||||||||||||
C. Choy, J. Gwak, S. Savarese: 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. CVPR 2019 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
L3DETR-ScanNet_200 | 0.336 6 | 0.533 9 | 0.279 4 | 0.155 6 | 0.508 4 | 0.073 9 | 0.101 13 | 0.000 1 | 0.058 2 | 0.000 1 | 0.294 12 | 0.233 12 | 0.548 3 | 0.927 1 | 0.788 7 | 0.264 2 | 0.463 7 | 0.000 3 | 0.638 8 | 0.098 11 | 0.014 5 | 0.411 9 | 0.226 9 | 0.525 9 | 0.225 7 | 0.010 4 | 0.397 4 | 0.000 1 | 0.000 3 | 0.192 4 | 0.380 10 | 0.598 4 | 0.000 1 | 0.117 4 | 0.000 2 | 0.883 5 | 0.082 7 | 0.689 3 | 0.000 6 | 0.032 13 | 0.549 5 | 0.417 4 | 0.910 4 | 0.000 1 | 0.000 2 | 0.448 7 | 0.613 7 | 0.000 9 | 0.697 6 | 0.960 1 | 0.759 3 | 0.158 2 | 0.293 2 | 0.883 5 | 0.000 1 | 0.312 3 | 0.583 2 | 0.079 4 | 0.422 10 | 0.068 13 | 0.660 6 | 0.418 6 | 0.298 8 | 0.430 10 | 0.114 8 | 0.526 3 | 0.776 2 | 0.051 3 | 0.679 1 | 0.946 5 | 0.152 6 | 0.000 1 | 0.183 5 | 0.000 11 | 0.211 6 | 0.511 8 | 0.409 12 | 0.565 8 | 0.355 6 | 0.448 5 | 0.512 4 | 0.557 2 | 0.000 3 | 0.000 1 | 0.420 7 | 0.000 8 | 0.007 13 | 0.104 4 | 0.000 3 | 0.125 13 | 0.330 2 | 0.514 11 | 0.146 8 | 0.321 9 | 0.860 7 | 0.174 7 | 0.000 1 | 0.629 4 | 0.075 12 | 0.000 10 | 0.000 4 | 0.000 1 | 0.002 6 | 0.671 5 | 0.712 4 | 0.141 5 | 0.339 8 | 0.856 3 | 0.261 8 | 0.529 8 | 0.067 8 | 0.835 2 | 0.000 4 | 0.369 10 | 0.000 1 | 0.259 2 | 0.000 3 | 0.629 4 | 0.000 2 | 0.487 1 | 0.000 1 | 0.579 9 | 0.646 3 | 0.107 13 | 0.720 9 | 0.122 6 | 0.000 1 | 0.333 10 | 0.505 8 | 0.303 6 | 0.908 2 | 0.503 11 | 0.565 2 | 0.074 7 | 0.324 1 | 0.000 1 | 0.740 6 | 0.661 7 | 0.109 10 | 0.000 8 | 0.427 9 | 0.563 13 | 0.000 1 | 0.579 8 | 0.108 6 | 0.000 5 | 0.000 1 | 0.664 4 | 0.000 2 | 0.000 1 | 0.641 6 | 0.539 7 | 0.416 5 | 0.515 2 | 0.256 7 | 0.940 9 | 0.312 4 | 0.209 13 | 0.620 2 | 0.138 10 | 0.636 9 | 0.000 1 | 0.000 9 | 0.775 9 | 0.861 4 | 0.765 9 | 0.000 1 | 0.801 8 | 0.119 11 | 0.860 6 | 0.000 1 | 0.687 1 | 0.001 10 | 0.192 12 | 0.679 7 | 0.699 1 | |||||||||||||||||||||||||||||
Yanmin Wu, Qiankun Gao, Renrui Zhang, Jian Zhang: Language-Assisted 3D Scene Understanding. arXiv23.12 |