Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells

Understand

Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation.

• Despite the excellent performance, existing methods ignore the fact that a suitable depth geometry is also critical in MVS.
• In this paper, we demonstrate that different depth geometries have significant performance gaps, even using the same depth prediction error.
• Therefore, we introduce an ideal depth geometry composed of Saddle-Shaped Cells, whose predicted depth map oscillates upward and downward around the ground-truth surface, rather than maintaining a continuous and smooth depth plane.