# Mesh Density Adaptation for Template-based Shape Reconstruction

arXiv: https://arxiv.org/abs/2307.16205 · HTML: https://mlsanity.com/paper/2307.16205/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 30 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [Modular Primitives for High-Performance Differentiable Rendering](https://mlsanity.com/paper/2011.03277/) (2020)
- [Neural 3D Mesh Renderer](https://mlsanity.com/paper/1711.07566/) (2017)
- [3D Menagerie: Modeling the 3D shape and pose of animals](https://mlsanity.com/paper/1611.07700/) (2016)
- [Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation](https://mlsanity.com/paper/1908.01491/) (2019)
- [Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering](https://mlsanity.com/paper/2103.15208/) (2021)
- [Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation](https://mlsanity.com/paper/1703.10131/) (2017)
- [Point2Mesh: A Self-Prior for Deformable Meshes](https://mlsanity.com/paper/2005.11084/) (2020)
- [Differentiable Refraction-Tracing for Mesh Reconstruction of Transparent Objects](https://mlsanity.com/paper/2009.09144/) (2020)
- [VectorAdam for Rotation Equivariant Geometry Optimization](https://mlsanity.com/paper/2205.13599/) (2022)
- [3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction](https://mlsanity.com/paper/2103.08204/) (2021)
- [Non-rigid 3D Shape Registration using an Adaptive Template](https://mlsanity.com/paper/1803.07973/) (2018)

## Built on by (most cited first, 7 of 7)

- [DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fields](https://mlsanity.com/paper/2401.09736/) (2024)
- [Semantic Human Mesh Reconstruction with Textures](https://mlsanity.com/paper/2403.02561/) (2024)
- [Neural Geometry Image-Based Representations with Optimal Transport (OT)](https://mlsanity.com/paper/2511.18679/) (2025)
- [Inverse Rendering for High-Genus Surface Meshes from Multi-View Images](https://mlsanity.com/paper/2511.18680/) (2025)
- [Towards Geometric-Photometric Joint Alignment for Facial Mesh Registration](https://mlsanity.com/paper/2403.02629/) (2024)
- [Inverse Rendering for High-Genus 3D Surface Meshes from Multi-view Images with Persistent Homology Priors](https://mlsanity.com/paper/2601.12155/) (2026)
- [RADmesh: Remesh-Aware Mesh Deformation](https://mlsanity.com/paper/2608.17182/) (2026)

## Abstract (arXiv)

In 3D shape reconstruction based on template mesh deformation, a regularization, such as smoothness energy, is employed to guide the reconstruction into a desirable direction. In this paper, we highlight an often overlooked property in the regularization: the vertex density in the mesh. Without careful control on the density, the reconstruction may suffer from under-sampling of vertices near shape details. We propose a novel mesh density adaptation method to resolve the under-sampling problem. Our mesh density adaptation energy increases the density of vertices near complex structures via deformation to help reconstruction of shape details. We demonstrate the usability and performance of mesh density adaptation with two tasks, inverse rendering and non-rigid surface registration. Our method produces more accurate reconstruction results compared to the cases without mesh density adaptation.