Semantic Segmentation On S3Dis
Metriken
Mean IoU
Number of params
oAcc
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Mean IoU | Number of params | oAcc |
|---|---|---|---|
| 190408017 | 62.9 | N/A | 87.3 |
| learning-multi-view-aggregation-in-the-wild | 74.7 | 41.2M | 90.1 |
| 191111236 | - | 1.2M | 87.1 |
| 4d-spatio-temporal-convnets-minkowski | 65.4 | 37.9M | - |
| mugnet-multi-resolution-graph-neural-network | 69.8 | N/A | 88.5 |
| a-unified-query-based-paradigm-for-point | 77.5 | N/A | - |
| fg-net-fast-large-scale-lidar-point | 70.8 | N/A | 88.2 |
| swin3d-a-pretrained-transformer-backbone-for | 79.8 | N/A | 92.4 |
| jsenet-joint-semantic-segmentation-and-edge | 67.7 | N/A | - |
| fully-automated-scan-to-bim-via-point-cloud | - | - | - |
| 190408017 | 65.4 | N/A | 88.1 |
| pointasnl-robust-point-clouds-processing | 68.7 | N/A | 88.8 |
| kpconv-flexible-and-deformable-convolution | 70.6 | 14.1M | - |
| point-transformer-1 | 65.4 | N/A | - |
| semantic-segmentation-for-real-point-cloud | 72.2 | N/A | 88.9 |
| towards-large-scale-3d-representation | 78.1 | N/A | 92.2 |
| 191209654 | 61.7 | N/A | 88.7 |
| shellnet-efficient-point-cloud-convolutional | 66.8 | N/A | - |
| 190408017 | 62.1 | N/A | 85.5 |
| 190408017 | 56.3 | N/A | 86.9 |
| point-cloud-oversegmentation-with-graph | 68.4 | 0.290M | 87.9 |
| pointweb-enhancing-local-neighborhood | 66.7 | N/A | 87.3 |
| meta-architecure-for-point-cloud-analysis | 77.0 | 19.7M | 91.3 |
| point-cloud-pre-training-by-mixing-and | 51.74 | N/A | - |
| point-transformer-v3-simpler-faster-stronger | 80.8 | 24.1M | 92.6 |
| lightconvpoint-convolution-for-points | 68.4 | N/A | - |
| point-transformer-1 | 62.1 | N/A | - |
| point-planenet-plane-kernel-based | 54.8 | N/A | 83.9 |
| large-scale-point-cloud-semantic-segmentation | 62.1 | 0.290M | 85.5 |
| dspoint-dual-scale-point-cloud-recognition | 63.3 | N/A | - |
| window-normalization-enhancing-point-cloud | 74.1 | 8.0M | 90.2 |
| generalizing-discrete-convolutions-for | 68.2 | 4.7M | 88.8 |
| window-normalization-enhancing-point-cloud | 77.6 | 8.2M | 91.7 |
| point-transformer-1 | 73.5 | 7.8M | 90.2 |
| scalable-3d-panoptic-segmentation-with | 75.3 | 0.21M | - |
| learning-inner-group-relations-on-point | 70.8 | N/A | - |
| 190408017 | 47.6 | N/A | 78.5 |
| cloud-transformers | 67.4 | N/A | - |
| surface-representation-for-point-clouds | 74.3 | 0.97M | 90.8 |
| associatively-segmenting-instances-and | 59.3 | N/A | - |
| contrastive-boundary-learning-for-point-cloud | 73.1 | N/A | 89.6 |
| point-transformer-1 | 47.6 | N/A | - |
| scf-net-learning-spatial-contextual-features | 71.6 | N/A | 88.4 |
| deepgcns-making-gcns-go-as-deep-as-cnns | 60.0 | N/A | 85.9 |
| fast-point-transformer | 70.3 | N/A | - |
| efficient-3d-semantic-segmentation-with-1 | 76.0 | 0.212M | 90.4 |
| pointnext-revisiting-pointnet-with-improved | 74.9 | 41.6M | 90.3 |
| ponderv2-pave-the-way-for-3d-foundataion | 79.9 | - | 92.5 |
| sonata-self-supervised-learning-of-reliable | 82.3 | 128M | 93.3 |
| pointnet-deep-learning-on-point-sets-for-3d | - | N/A | - |
| point-is-a-vector-a-feature-representation-in | 78.4 | - | 91.9 |
| pointnext-revisiting-pointnet-with-improved | 73.9 | 7.1M | 89.9 |
| point-transformer-1 | 70.6 | 14.1M | - |
| recurrent-slice-networks-for-3d-segmentation | 56.5 | N/A | - |