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Models/PointNet++ (Xiang 2023)

PointNet++ (Xiang 2023)

Reported on 12 benchmarks across 3 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical4 results

Semantic SegmentationonS3DIS Area5
PQ· 2023-04-27
24.6
best: 50.1 (SuperCluster)
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980
Semantic SegmentationonS3DIS Area5
Params (M)· 2023-04-27
3.1
best: 38 (MinkowskiNet (Xiang 2023))
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980
Semantic SegmentationonS3DIS Area5
RQ· 2023-04-27
32.6
best: 60.1 (SuperCluster)
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980
Semantic SegmentationonS3DIS Area5
SQ· 2023-04-27
68.2
best: 76.6 (SuperCluster)
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980

Audio4 results

10-shot image generationonS3DIS Area5
PQ· 2023-04-27
24.6
best: 50.1 (SuperCluster)
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980
10-shot image generationonS3DIS Area5
Params (M)· 2023-04-27
3.1
best: 38 (MinkowskiNet (Xiang 2023))
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980
10-shot image generationonS3DIS Area5
RQ· 2023-04-27
32.6
best: 60.1 (SuperCluster)
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980
10-shot image generationonS3DIS Area5
SQ· 2023-04-27
68.2
best: 76.6 (SuperCluster)
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980

Computer Vision4 results

Panoptic SegmentationonS3DIS Area5
PQ· 2023-04-27
24.6
best: 50.1 (SuperCluster)
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980
Panoptic SegmentationonS3DIS Area5
Params (M)· 2023-04-27
3.1
best: 38 (MinkowskiNet (Xiang 2023))
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980
Panoptic SegmentationonS3DIS Area5
RQ· 2023-04-27
32.6
best: 60.1 (SuperCluster)
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980
Panoptic SegmentationonS3DIS Area5
SQ· 2023-04-27
68.2
best: 76.6 (SuperCluster)
A Review of Panoptic Segmentation for Mobile Mapping Point Clouds arXiv:2304.13980