Papers › PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic...

PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic Segmentation

24 Dec 2023Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 12archive 2025-07-28

Anh-Thuan Tran, Hoanh-Su Le, Suk-Hwan Lee, Ki-Ryong Kwon

Although point cloud segmentation has a principal role in 3D understanding, annotating fully large-scale scenes for this task can be costly and time-consuming. To resolve this issue, we propose Point Central Transformer (PointCT), a novel end-to-end trainable transformer network for weakly-supervised point cloud semantic segmentation. Divergent from prior approaches, our method addresses limited point annotation challenges exclusively based on 3D points through central-based attention. By employing two embedding processes, our attention mechanism integrates global features across neighborhoods, thereby effectively enhancing unlabeled point representations. Simultaneously, the interconnections between central points and their distinct neighborhoods are bidirectional cohered. Position encoding is further applied to enforce geometric features and improve overall performance. Notably, PointCT achieves outstanding performance under various labeled point settings without additional supervision. Extensive experiments on public datasets S3DIS, ScanNet-V2, and STPLS3D demonstrate the superiority of our proposed approach over other state-of-the-art methods.

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anhthuan1999/PointCT officialpytorch report

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Tasks

3D Semantic Segmentation Point Cloud Segmentation Segmentation Semantic Segmentation Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
3D Semantic Segmentation	STPLS3D	PointCT	mIOU	53.2	#2 of 6	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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