Improve 3D object segmentation using transformers directly on point clouds with Mask3D
Improve 3D object segmentation using transformers directly on point clouds with Mask3D
Mask3D for 3D Semantic Instance Segmentation
arXiv paper abstract https://arxiv.org/abs/2210.03105v1
arXiv PDF paper https://arxiv.org/pdf/2210.03105v1.pdf
Modern 3D semantic instance segmentation approaches predominantly rely on specialized voting mechanisms followed by carefully designed geometric clustering techniques.
... propose the first Transformer-based approach for 3D semantic instance segmentation ... to directly predict instance masks from 3D point clouds.
In ... Mask3D .. Using Transformer decoders, the instance queries are learned by iteratively attending to point cloud features at multiple scales.
Combined with point features, the instance queries directly yield all instance masks in parallel.
Mask3D has several advantages over current state-of-the-art approaches, since it neither relies on (1) voting schemes which require hand-selected geometric properties (such as centers) nor (2) geometric grouping mechanisms requiring manually-tuned hyper-parameters (e.g. radii) and (3) enables a loss that directly optimizes instance masks.
Mask3D sets a new state-of-the-art on ScanNet test ...
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