Transformer-Based 3D Point Cloud Object Recognition for Autonomous Vehicles

Engineering notes

Key topics: autonomous driving, autonomous vehicle, lidar, point cloud, deployment, perception. See the paper for implementation details and experimental results.

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

Object recognition is essential for secure and effective navigation via accurate scene interpretation in autonomous vehicle perception systems. 3D point clouds created by Light Detection and Ranging (LiDAR) systems provide geometric and spatial information, making them ideal for item detection and classification in chaotic and ever-changing settings. Regarding irregular 3D data, traditional deep learning methods like PointNet and PointNet++ can only model localized features; they cannot handle global context or long-range relationships. In addition to limiting real-time deployment owing to processing costs, these limitations lower detection performance in complex environments. This study introduces a 3D Transformer-based Object Recognition Architecture for Vehicles (3D-TORAV) autonomous driving environments. Acquiring both local geometric features and global spatial interdependence concurrently is achieved by integrating multi-head self-attention mechanisms in the suggested architecture. Hierarchical attention layers improve feature representation, while a customized 3D positional encoding approach preserves geometric consistency. Comprehensive object identification is facilitated by a classification and bounding box regression head. Various annotated driving scenarios were used to train and test the model. Concerning accuracy and mean Average Precision (mAP), the suggested framework outperformed the existing models.

Links and sources

• Official / arXiv page

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