FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction

Understand

Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency.

• However, the reconstructed geometry, typically represented as a 3D truncated signed distance function (TSDF), is often coarse without fine geometric details.
• To address this problem, we propose three effective solutions for improving the fidelity of inference-based 3D reconstructions.
• We first present a resolution-agnostic TSDF supervision strategy to provide the network with a more accurate learning signal during training, avoiding the pitfalls of TSDF interpolation seen in previous work.