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

Engineering notes

Extensive experiments on the KITTI dataset demonstrate that PEPF-Net outperforms the currently common advanced environmental 3D perception algorithms.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

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.

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