Summary of Nd-sdf: Learning Normal Deflection Fields For High-fidelity Indoor Reconstruction, by Ziyu Tang et al.
ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction
by Ziyu Tang, Weicai Ye, Yifan Wang, Di Huang, Hujun Bao, Tong He, Guofeng Zhang
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes ND-SDF, a novel neural implicit reconstruction method that learns a Normal Deflection field to adaptively utilize samples based on their characteristics. Unlike previous methods that employ geometric priors, ND-SDF dynamically learns and adapts the utilization of samples, improving both accuracy and effectiveness. The method not only preserves smooth weakly textured regions but also captures geometric details of complex structures. A novel ray sampling strategy is introduced to facilitate unbiased rendering, leading to improved quality and accuracy of intricate surfaces. Experimental results on various challenging datasets demonstrate the superiority of ND-SDF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to build 3D models from incomplete data. It uses special fields called “normal deflection” to help it understand how to use different parts of the data together. This helps the model create more accurate and detailed 3D surfaces, especially for complex structures like buildings or machines. |
