# Patch-Grid: An Efficient and Feature-Preserving Neural Implicit Surface Representation

arXiv: https://arxiv.org/abs/2308.13934 · HTML: https://mlsanity.com/paper/2308.13934/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2023.

## Built on by (most cited first, 4 of 4)

- [NeuroBind: Towards Unified Multimodal Representations for Neural Signals](https://mlsanity.com/paper/2407.14020/) (2024)
- [Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization](https://mlsanity.com/paper/2401.12946/) (2024)
- [Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches](https://mlsanity.com/paper/2309.13006/) (2023)
- [CEI-3D: Collaborative Explicit-Implicit 3D Reconstruction for Realistic and Fine-Grained Object Editing](https://mlsanity.com/paper/2603.11810/) (2026)

## Abstract (arXiv)

Neural implicit representations are widely used for 3D shape modeling due to their smoothness and compactness, but traditional MLP-based methods struggle with sharp features, such as edges and corners in CAD models, and require long training times. To address these limitations, we propose Patch-Grid, a unified neural implicit representation that efficiently fits complex shapes, preserves sharp features, and handles open boundaries and thin geometric structures. Patch-Grid learns a signed distance field (SDF) for each surface patch using a learnable patch feature volume. To represent sharp edges and corners, it merges the learned SDFs via constructive solid geometry (CSG) operations. A novel merge grid organizes patch feature volumes within a shared octree structure, localizing and simplifying CSG operations. This design ensures robust merging of SDFs and significantly reduces computational complexity, enabling training within seconds while maintaining high fidelity. Experimental results show that Patch-Grid achieves state-of-the-art reconstruction quality for shapes with intricate sharp features, open surfaces, and thin structures, offering superior robustness, efficiency, and accuracy.