CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction
This work addresses 3D object detection for autonomous driving or robotics, but it is incremental as it builds on existing methods like Cross-View Transformers and CenterPoint.
The paper tackles the problem of 3D object detection by combining LiDAR and camera data, proposing CVCP-Fusion to preserve semantic information from camera features while incorporating spatial data from LiDAR, achieving state-of-the-art performance with efficient real-time processing.
Combining LiDAR and Camera-view data has become a common approach for 3D Object Detection. However, previous approaches combine the two input streams at a point-level, throwing away semantic information derived from camera features. In this paper we propose Cross-View Center Point-Fusion, a state-of-the-art model to perform 3D object detection by combining camera and LiDAR-derived features in the BEV space to preserve semantic density from the camera stream while incorporating spacial data from the LiDAR stream. Our architecture utilizes aspects from previously established algorithms, Cross-View Transformers and CenterPoint, and runs their backbones in parallel, allowing efficient computation for real-time processing and application. In this paper we find that while an implicitly calculated depth-estimate may be sufficiently accurate in a 2D map-view representation, explicitly calculated geometric and spacial information is needed for precise bounding box prediction in the 3D world-view space.