3D Object Detection Based on Sparse Feature Extraction and Semantic Fusion from LiDAR Point Clouds

Engineering notes

Finally, our model has been rigorously evaluated on the nuScenes 3D object detection benchmark.

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

Accurate 3D object detection from point clouds is crucial for the advancement of autonomous vehicle technologies. However, LiDAR sensors often produce inherently sparse point cloud data, particularly at longer ranges, posing significant challenges for point cloud-based 3D object detection. The absence of fixed spatial relationships between points further complicates the computation of receptive fields during convolutions and other point cloud processing operations. To tackle these issues, we propose a novel method for 3D object detection based on Sparse Feature Extraction and Semantic Fusion from LiDAR point clouds. First, we design a novel Feature Extraction Module (FEM) that employs a sparsity-driven feature detection and extraction strategy. This strategy focuses on enhancing features extracted from different spatial domains and progressively expanding receptive fields to aggregate critical contextual information. Second, we develop a Spatial-Semantic Feature Fusion Module (SSFFM) designed to effectively combine features extracted from various levels. This module leverages multi-level feature maps to facilitate information exchange between different feature maps, thereby improving adaptability to variations in object size. Finally, our model has been rigorously evaluated on the nuScenes 3D object detection benchmark. Experimental results show a significant improvement in detection precision compared to traditional methods, demonstrating the robustness and effectiveness of our proposed approach.

Links and sources

• Official / arXiv page

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