Neural 3D Scene Reconstruction with the Manhattan-world Assumption

Understand

This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images.

• Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions, which are common in indoor scenes.
• An approach to solving this issue is to incorporate planer constraints into the depth map estimation in multi-view stereo-based methods, but the per-view plane estimation and depth optimization lack both efficiency and multi-view consistency.
• In this work, we show that the planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods.