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<p>Based on the complementary and enhanced fusion of 3D point clouds and 2D RGB images, this paper designs an end-to-end learning framework—Point Cloud Enhanced Depth Pixel Fusion Network (PEPF-Net), aimed at enabling robots to achieve accurate 3D perception of unstructured environments. In the process, we address four key problems in 3D perception tasks: enhancing RGB representation using the reflection intensity and depth information of point clouds to generate Depth-RGB Pixel (D-Pixel); proposing Point-by-Point Vector Attention (PVA-Net) to model the vector relationships of point clouds, &#xa0;to obtain deep-level point cloud features, and to achieve direct and effective fusion of heterogeneous data; designing a Layered-Transformer (L-TsfmNet) feature extractor to hierarchically extract D-Pixel features; proposing Variable Window Self-attention (VS-a) to focus on the relationships between local “window tokens” and avoid the complexity of global computation. Extensive experiments on the KITTI dataset demonstrate that PEPF-Net outperforms the currently common advanced environmental 3D perception algorithms.</p>

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3D perception algorithm of unstructured environment based on point cloud enhanced pixel fusion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月21日

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  • Guo Chen,
  • Liming Wan,
  • Jingjing He,
  • Shuhao Jiang,
  • Lin Song

摘要

Based on the complementary and enhanced fusion of 3D point clouds and 2D RGB images, this paper designs an end-to-end learning framework—Point Cloud Enhanced Depth Pixel Fusion Network (PEPF-Net), aimed at enabling robots to achieve accurate 3D perception of unstructured environments. In the process, we address four key problems in 3D perception tasks: enhancing RGB representation using the reflection intensity and depth information of point clouds to generate Depth-RGB Pixel (D-Pixel); proposing Point-by-Point Vector Attention (PVA-Net) to model the vector relationships of point clouds,  to obtain deep-level point cloud features, and to achieve direct and effective fusion of heterogeneous data; designing a Layered-Transformer (L-TsfmNet) feature extractor to hierarchically extract D-Pixel features; proposing Variable Window Self-attention (VS-a) to focus on the relationships between local “window tokens” and avoid the complexity of global computation. Extensive experiments on the KITTI dataset demonstrate that PEPF-Net outperforms the currently common advanced environmental 3D perception algorithms.