RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Semantic Segmentation S3DIS RandLA-Net Mean IoU 70.0 # 3
mAcc 82.0 # 1
oAcc 88.0 # 4
Semantic Segmentation Semantic3D RandLA-Net mIoU 76.0% # 1
3D Semantic Segmentation SemanticKITTI RandLA-Net mIoU 53.9% # 13

Methods used in the Paper


METHOD TYPE
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