Vision Foundation Model Driven Foreground-Aware Pseudo-LiDAR Generation for Monocular 3D Object Detection

Understand

Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations that can be processed by LiDAR-based 3D object detectors.

• Recent vision foundation models provide powerful geometric and semantic priors, creating new opportunities for improving the quality of pseudo-LiDAR generation.
• However, effectively exploiting these priors to produce reliable pseudo-LiDAR remains challenging due to inaccurate depth estimation, insufficient foreground awareness, and redundant background points.In this paper, we propose VFMM3D, a vision foundation model-driven framework for foreground-aware pseudo-LiDAR generation.
• VFMM3D leverages the depth prior provided by the Depth Anything Model (DAM) and the foreground prior provided by the Segment Anything Model (SAM).