StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision

Understand

In this paper, we propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human from a pair of low-cost rectified images.

• First, we introduce the effective voxel-aligned features from a stereo vision-based network to enable depth-aware reconstruction.
• Moreover, the novel relative z-offset is employed to associate predicted high-fidelity human depth and occupancy inference, which helps restore fine-level surface details.
• Second, a network structure that fully utilizes the geometry information from the stereo images is designed to improve the human body reconstruction quality.