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Reconstructing the 3D shape of objects observed in a single image is a challenging task. Recent approaches rely on visual cues extracted from a given image learned from a deep net. In this work, we leverage recent advances in monocular scene understanding to incorporate an additional geometric cue of surface normals. For this, we proposed a novel optimization layer that encourages the face normals of the reconstructed shape to be aligned with estimated surface normals. We develop a computationally efficient conjugate-gradient-based method that avoids the computation of a high-dimensional sparse matrix. We show this framework to achieve compelling shape reconstruction results on the challenging Pix3D and ShapeNet datasets.
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This content will become publicly available on August 1, 2026
Variational Surface Reconstruction Using Natural Neighbors
Surface reconstruction from points is a fundamental problem in computer graphics. While numerous methods have been proposed, it remains challenging to reconstruct from sparse and non-uniform point distributions, particularly when normals are absent. We present a robust and scalable method for reconstructing an implicit surface from points without normals. By exploring the locality of natural neighborhoods, we propose local reformulations of a previous global method, known for its ability to surface sparse points but high computational cost, thereby significantly improving its scalability while retaining its robustness. Experiments show that our method achieves comparable speed to existing reconstruction methods on large inputs while producing fewer artifacts in under-sampled regions.
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- Award ID(s):
- 2401224
- PAR ID:
- 10630449
- Publisher / Repository:
- Association for Computing Machinery
- Date Published:
- Journal Name:
- ACM Transactions on Graphics
- Volume:
- 44
- Issue:
- 4
- ISSN:
- 0730-0301
- Page Range / eLocation ID:
- 1 to 19
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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