# MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image

arXiv: https://arxiv.org/abs/1811.09410 · HTML: https://mlsanity.com/paper/1811.09410/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2018.

## Built on by (most cited first, 17 of 17)

- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [Point-NeRF: Point-based Neural Radiance Fields](https://mlsanity.com/paper/2201.08845/) (2022)
- [NeuTex: Neural Texture Mapping for Volumetric Neural Rendering](https://mlsanity.com/paper/2103.00762/) (2021)
- [3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow](https://mlsanity.com/paper/2203.15190/) (2022)
- [Strivec: Sparse Tri-Vector Radiance Fields](https://mlsanity.com/paper/2307.13226/) (2023)
- [SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators](https://mlsanity.com/paper/2201.00112/) (2022)
- [RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes](https://mlsanity.com/paper/2502.15633/) (2025)
- [Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network](https://mlsanity.com/paper/2208.00183/) (2022)
- [Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering](https://mlsanity.com/paper/2408.06286/) (2024)
- [Learning Naturally Aggregated Appearance for Efficient 3D Editing](https://mlsanity.com/paper/2312.06657/) (2023)
- [SceneFactory: A Workflow-centric and Unified Framework for Incremental Scene Modeling](https://mlsanity.com/paper/2405.07847/) (2024)
- [Anisotropic Neural Representation Learning for High-Quality Neural Rendering](https://mlsanity.com/paper/2311.18311/) (2023)
- [DDR-Net: Learning Multi-Stage Multi-View Stereo With Dynamic Depth Range](https://mlsanity.com/paper/2103.14275/) (2021)
- [Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images](https://mlsanity.com/paper/2109.11844/) (2021)
- [Reconstructing vehicles from orthographic drawings using deep neural networks](https://mlsanity.com/paper/2206.08789/) (2022)
- [UNeR3D: Versatile and Scalable 3D RGB Point Cloud Generation from 2D Images in Unsupervised Reconstruction](https://mlsanity.com/paper/2312.06706/) (2023)
- [Point Resampling and Ray Transformation Aid to Editable NeRF Models](https://mlsanity.com/paper/2405.07306/) (2024)

## Abstract (arXiv)

In this paper, we address the problem of reconstructing an object's surface from a single image using generative networks. First, we represent a 3D surface with an aggregation of dense point clouds from multiple views. Each point cloud is embedded in a regular 2D grid aligned on an image plane of a viewpoint, making the point cloud convolution-favored and ordered so as to fit into deep network architectures. The point clouds can be easily triangulated by exploiting connectivities of the 2D grids to form mesh-based surfaces. Second, we propose an encoder-decoder network that generates such kind of multiple view-dependent point clouds from a single image by regressing their 3D coordinates and visibilities. We also introduce a novel geometric loss that is able to interpret discrepancy over 3D surfaces as opposed to 2D projective planes, resorting to the surface discretization on the constructed meshes. We demonstrate that the multi-view point regression network outperforms state-of-the-art methods with a significant improvement on challenging datasets.