A multi-scale 3D scene flow estimation network based on transformer

Yingxin Wei; Liwei Chen

doi:10.1117/12.3039120

13 September 2024 A multi-scale 3D scene flow estimation network based on transformer

Yingxin Wei, Liwei Chen

Proceedings Volume 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024); 1325410 (2024) https://doi.org/10.1117/12.3039120
Event: Fourth International Conference on Optics and Image Processing (ICOIP 2024), 2024, Chongqing, China

Abstract

The purpose of scene flow estimation is to capture the intricate motion patterns within point clouds across successive frames. We incorporate the channel self-attention (CSA) into the estimation of scene flow for point clouds. Specifically, the channel self-attention mechanism prioritizes channels with significant disparities to prevent the merging of similar and redundant information. Through the subtraction operation embedded in the structure, attention weights are concentrated in regions with salient characteristics and crucial information within the point cloud, thereby reducing attention toward noise points. By incorporating channel self-attention at each stage of the network, we can extract local features and capture rich contextual information. Additionally, we introduce a channel excitation module to enhance the global correlation among channels and enhance the model's representation capability by introducing additional nonlinear relationships. Experimental results verify that our proposed method is effective.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yingxin Wei and Liwei Chen "A multi-scale 3D scene flow estimation network based on transformer", Proc. SPIE 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024), 1325410 (13 September 2024); https://doi.org/10.1117/12.3039120

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KEYWORDS

Point clouds

Feature extraction

Transformers

Education and training

3D modeling

Motion estimation

Motion models

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