# Perspective-aware fusion of incomplete depth maps and surface normals for accurate 3D reconstruction

arXiv: https://arxiv.org/abs/2602.07444 · HTML: https://mlsanity.com/paper/2602.07444/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 7 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials](https://mlsanity.com/paper/2001.06659/) (2020)
- [Normal Integration: A Survey](https://mlsanity.com/paper/1709.05940/) (2017)
- [SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration](https://mlsanity.com/paper/2312.04803/) (2023)
- [Discontinuity-preserving Normal Integration with Auxiliary Edges](https://mlsanity.com/paper/2404.03138/) (2024)

## Abstract (arXiv)

We address the problem of reconstructing 3D surfaces from depth and surface normal maps acquired by a sensor system based on a single perspective camera. Depth and normal maps can be obtained through techniques such as structured-light scanning and photometric stereo, respectively. We propose a perspective-aware log-depth fusion approach that extends existing orthographic gradient-based depth-normals fusion methods by explicitly accounting for perspective projection, leading to metrically accurate 3D reconstructions. Additionally, the method handles missing depth measurements by leveraging available surface normal information to inpaint gaps. Experiments on the DiLiGenT-MV data set demonstrate the effectiveness of our approach and highlight the importance of perspective-aware depth-normals fusion.