# Better Patch Stitching for Parametric Surface Reconstruction

arXiv: https://arxiv.org/abs/2010.07021 · HTML: https://mlsanity.com/paper/2010.07021/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks](https://mlsanity.com/paper/2103.10429/) (2021)
- [The Power of Points for Modeling Humans in Clothing](https://mlsanity.com/paper/2109.01137/) (2021)
- [SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements](https://mlsanity.com/paper/2104.07660/) (2021)
- [SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization](https://mlsanity.com/paper/2104.09125/) (2021)
- [Learning Implicit Templates for Point-Based Clothed Human Modeling](https://mlsanity.com/paper/2207.06955/) (2022)
- [An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion](https://mlsanity.com/paper/2408.03178/) (2024)
- [ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction](https://mlsanity.com/paper/2212.04096/) (2022)
- [CloSET: Modeling Clothed Humans on Continuous Surface with Explicit Template Decomposition](https://mlsanity.com/paper/2304.03167/) (2023)
- [Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases](https://mlsanity.com/paper/2104.06950/) (2021)
- [Sketch2PQ: Freeform Planar Quadrilateral Mesh Design via a Single Sketch](https://mlsanity.com/paper/2201.09367/) (2022)
- [Minimal Neural Atlas: Parameterizing Complex Surfaces with Minimal Charts and Distortion](https://mlsanity.com/paper/2207.14782/) (2022)
- [Mesh Draping: Parametrization-Free Neural Mesh Transfer](https://mlsanity.com/paper/2110.05433/) (2021)
- [Modeling 3D Surface Manifolds with a Locally Conditioned Atlas](https://mlsanity.com/paper/2102.05984/) (2021)
- [Learning Joint Surface Atlases](https://mlsanity.com/paper/2206.06273/) (2022)
- [FreeCloth: Free-form Generation Enhances Challenging Clothed Human Modeling](https://mlsanity.com/paper/2411.19942/) (2024)
- [BPNet: B'ezier Primitive Segmentation on 3D Point Clouds](https://mlsanity.com/paper/2307.04013/) (2023)
- [Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases](https://mlsanity.com/paper/2111.06838/) (2021)
- [NESI: Shape Representation via Neural Explicit Surface Intersection](https://mlsanity.com/paper/2409.06030/) (2024)
- [Patch-based Representation and Learning for Efficient Deformation Modeling](https://mlsanity.com/paper/2601.05035/) (2026)
- [SwiftTailor: Efficient 3D Garment Generation with Geometry Image Representation](https://mlsanity.com/paper/2603.19053/) (2026)
- [Blended Chart Surfaces: A Seamless Explicit Representation for Smooth Surface Fitting](https://mlsanity.com/paper/2606.18069/) (2026)

## Abstract (arXiv)

Recently, parametric mappings have emerged as highly effective surface representations, yielding low reconstruction error. In particular, the latest works represent the target shape as an atlas of multiple mappings, which can closely encode object parts. Atlas representations, however, suffer from one major drawback: The individual mappings are not guaranteed to be consistent, which results in holes in the reconstructed shape or in jagged surface areas. We introduce an approach that explicitly encourages global consistency of the local mappings. To this end, we introduce two novel loss terms. The first term exploits the surface normals and requires that they remain locally consistent when estimated within and across the individual mappings. The second term further encourages better spatial configuration of the mappings by minimizing novel stitching error. We show on standard benchmarks that the use of normal consistency requirement outperforms the baselines quantitatively while enforcing better stitching leads to much better visual quality of the reconstructed objects as compared to the state-of-the-art.