# Learning View Priors for Single-view 3D Reconstruction

arXiv: https://arxiv.org/abs/1811.10719 · HTML: https://mlsanity.com/paper/1811.10719/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 30 of 35)

- [SyncDreamer: Generating Multiview-consistent Images from a Single-view Image](https://mlsanity.com/paper/2309.03453/) (2023)
- [MagicPony: Learning Articulated 3D Animals in the Wild](https://mlsanity.com/paper/2211.12497/) (2022)
- [LN3DIFF++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation](https://mlsanity.com/paper/2403.12019/) (2024)
- [PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction](https://mlsanity.com/paper/2302.10668/) (2023)
- [Shelf-Supervised Mesh Prediction in the Wild](https://mlsanity.com/paper/2102.06195/) (2021)
- [DOVE: Learning Deformable 3D Objects by Watching Videos](https://mlsanity.com/paper/2107.10844/) (2021)
- [Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset](https://mlsanity.com/paper/2206.15436/) (2022)
- [When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models](https://mlsanity.com/paper/2405.10255/) (2024)
- [Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis](https://mlsanity.com/paper/2201.01683/) (2022)
- [EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild](https://mlsanity.com/paper/2411.14280/) (2024)
- [VQ3D: Learning a 3D-Aware Generative Model on ImageNet](https://mlsanity.com/paper/2302.06833/) (2023)
- [Pre-train, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction](https://mlsanity.com/paper/2204.03642/) (2022)
- [Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency](https://mlsanity.com/paper/2204.10310/) (2022)
- [ShapeClipper: Scalable 3D Shape Learning from Single-View Images via Geometric and CLIP-based Consistency](https://mlsanity.com/paper/2304.06247/) (2023)
- [ViewFusion: Towards Multi-View Consistency via Interpolated Denoising](https://mlsanity.com/paper/2402.18842/) (2024)
- [Discovering 3D Parts from Image Collections](https://mlsanity.com/paper/2107.13629/) (2021)
- [Structural Causal 3D Reconstruction](https://mlsanity.com/paper/2207.10156/) (2022)
- [Efficient and Differentiable Shadow Computation for Inverse Problems](https://mlsanity.com/paper/2104.00359/) (2021)
- [ADOP: Approximate Differentiable One-Pixel Point Rendering](https://mlsanity.com/paper/2110.06635/) (2021)
- [Test-time Adaptation with Slot-Centric Models](https://mlsanity.com/paper/2203.11194/) (2022)
- [Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues](https://mlsanity.com/paper/2204.10235/) (2022)
- [Creative Birds: Self-Supervised Single-View 3D Style Transfer](https://mlsanity.com/paper/2307.14127/) (2023)
- [Multi-Category Mesh Reconstruction From Image Collections](https://mlsanity.com/paper/2110.11256/) (2021)
- [Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction](https://mlsanity.com/paper/2112.12484/) (2021)
- [3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective](https://mlsanity.com/paper/2204.13096/) (2022)
- [LIM: Large Interpolator Model for Dynamic Reconstruction](https://mlsanity.com/paper/2503.22537/) (2025)
- [Fine-Grained Adversarial Semi-supervised Learning](https://mlsanity.com/paper/2110.05848/) (2021)
- [PILA: Physics-Informed Low Rank Augmentation for Interpretable Earth Observation](https://mlsanity.com/paper/2405.18953/) (2024)
- [ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass](https://mlsanity.com/paper/2106.03382/) (2021)
- [Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method](https://mlsanity.com/paper/2308.10003/) (2023)

## Abstract (arXiv)

There is some ambiguity in the 3D shape of an object when the number of observed views is small. Because of this ambiguity, although a 3D object reconstructor can be trained using a single view or a few views per object, reconstructed shapes only fit the observed views and appear incorrect from the unobserved viewpoints. To reconstruct shapes that look reasonable from any viewpoint, we propose to train a discriminator that learns prior knowledge regarding possible views. The discriminator is trained to distinguish the reconstructed views of the observed viewpoints from those of the unobserved viewpoints. The reconstructor is trained to correct unobserved views by fooling the discriminator. Our method outperforms current state-of-the-art methods on both synthetic and natural image datasets; this validates the effectiveness of our method.