SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration

Xu Cao, Takafumi Taketomi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 20581-20590

We present SuperNormal a fast high-fidelity approach to multi-view 3D reconstruction using surface normal maps. With a few minutes SuperNormal produces detailed surfaces on par with 3D scanners. We harness volume rendering to optimize a neural signed distance function (SDF) powered by multi-resolution hash encoding. To accelerate training we propose directional finite difference and patchbased ray marching to approximate the SDF gradients numerically. While not compromising reconstruction quality this strategy is nearly twice as efficient as analytical gradients and about three times faster than axis-aligned finite difference. Experiments on the benchmark dataset demonstrate the superiority of SuperNormal in efficiency and accuracy compared to existing multi-view photometric stereo methods. On our captured objects SuperNormal produces more fine-grained geometry than recent neural 3D reconstruction methods. Our code is available at https://github.com/CyberAgentAILab/SuperNormal.git.