# Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images

arXiv: https://arxiv.org/abs/2008.07760 · HTML: https://mlsanity.com/paper/2008.07760/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 22 of 22)

- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [BEHAVE: Dataset and Method for Tracking Human Object Interactions](https://mlsanity.com/paper/2204.06950/) (2022)
- [BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects](https://mlsanity.com/paper/2303.14158/) (2023)
- [CHORE: Contact, Human and Object REconstruction from a single RGB image](https://mlsanity.com/paper/2204.02445/) (2022)
- [An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion](https://mlsanity.com/paper/2408.03178/) (2024)
- [ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes](https://mlsanity.com/paper/2201.07788/) (2022)
- [StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects](https://mlsanity.com/paper/2105.08016/) (2021)
- [A Real World Dataset for Multi-view 3D Reconstruction](https://mlsanity.com/paper/2203.11397/) (2022)
- [Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields](https://mlsanity.com/paper/2212.02493/) (2022)
- [NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation](https://mlsanity.com/paper/2206.05837/) (2022)
- [GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes](https://mlsanity.com/paper/2106.13215/) (2021)
- [Multi-Category Mesh Reconstruction From Image Collections](https://mlsanity.com/paper/2110.11256/) (2021)
- [TreeSketchNet: From Sketch To 3D Tree Parameters Generation](https://mlsanity.com/paper/2207.12297/) (2022)
- [Deformable Surface Reconstruction via Riemannian Metric Preservation](https://mlsanity.com/paper/2212.11596/) (2022)
- [Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian](https://mlsanity.com/paper/2203.15235/) (2022)
- [Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art](https://mlsanity.com/paper/2311.10129/) (2023)
- [TIFu: Tri-directional Implicit Function for High-Fidelity 3D Character Reconstruction](https://mlsanity.com/paper/2401.14565/) (2024)
- [Dynamic Scene 3D Reconstruction of an Uncooperative Resident Space Object](https://mlsanity.com/paper/2509.07932/) (2025)
- [StylePart: Image-based Shape Part Manipulation](https://mlsanity.com/paper/2111.10520/) (2021)
- [GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity](https://mlsanity.com/paper/2505.11905/) (2025)
- [3DMPE: 3D Multi-Perspective Embedding](https://mlsanity.com/paper/2607.04898/) (2026)
- [AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction](https://mlsanity.com/paper/2609.08493/) (2026)

## Abstract (arXiv)

We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views. Previous work on learning shape reconstruction from multiple views uses discrete representations such as point clouds or voxels, while continuous surface generation approaches lack multi-view consistency. We address these issues by designing neural networks capable of generating high-quality parametric 3D surfaces which are also consistent between views. Furthermore, the generated 3D surfaces preserve accurate image pixel to 3D surface point correspondences, allowing us to lift texture information to reconstruct shapes with rich geometry and appearance. Our method is supervised and trained on a public dataset of shapes from common object categories. Quantitative results indicate that our method significantly outperforms previous work, while qualitative results demonstrate the high quality of our reconstructions.