Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion

Understand

3D semantic scene completion (SSC) is an ill-posed perception task that requires inferring a dense 3D scene from limited observations.

• Previous camera-based methods struggle to predict accurate semantic scenes due to inherent geometric ambiguity and incomplete observations.
• In this paper, we resort to stereo matching technique and bird's-eye-view (BEV) representation learning to address such issues in SSC.
• Complementary to each other, stereo matching mitigates geometric ambiguity with epipolar constraint while BEV representation enhances the hallucination ability for invisible regions with global semantic context.