Papers › Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation

Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation

27 Apr 2023arXiv:2304.14124archive 2025-07-28

Zihao Li, Pan Gao, Hui Yuan, Ran Wei, Manoranjan Paul

Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature extractors while ignoring global connection, or vice versa. In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point relations, which considers both local and global attentions. Specifically, considering local spatial coherence, local feature learning is performed through Relative Position Encoding and Attentive Feature Pooling. We incorporate the learned locality into the Transformer module. The local feature affects value component in Transformer to modulate the relationship between channels of each point, which can enhance self-attention mechanism with locality based channel interaction. We demonstrate its superiority experimentally on classification and segmentation tasks. The code is available at: https://github.com/jiamang/IBT

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Code

jiamang/ibt officialmentioned in paperpytorch report

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Tasks

3D Object Classification 3D Part Segmentation Inductive Bias Point Cloud Classification

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Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
3D Object Classification	ModelNet40	Ours	Classification Accuracy	93.6	#1 of 7	Archive leaderboard	report
3D Part Segmentation	ShapeNet-Part	Ours	Instance Average IoU	86.2	#34 of 67	Archive leaderboard	report
Point Cloud Classification	ISPRS	Ours	Average F1	82.8	#1 of 1	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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