# Category-Specific Object Reconstruction from a Single Image

arXiv: https://arxiv.org/abs/1411.6069 · HTML: https://mlsanity.com/paper/1411.6069/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2014.

## Built on by (most cited first, 30 of 56)

- [Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors](https://mlsanity.com/paper/2306.17843/) (2023)
- [ABO: Dataset and Benchmarks for Real-World 3D Object Understanding](https://mlsanity.com/paper/2110.06199/) (2021)
- [SAM 3D: 3Dfy Anything in Images](https://mlsanity.com/paper/2511.16624/) (2025)
- [Monocular Dynamic View Synthesis: A Reality Check](https://mlsanity.com/paper/2210.13445/) (2022)
- [NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild](https://mlsanity.com/paper/2110.07604/) (2021)
- [MagicPony: Learning Articulated 3D Animals in the Wild](https://mlsanity.com/paper/2211.12497/) (2022)
- [CHORE: Contact, Human and Object REconstruction from a single RGB image](https://mlsanity.com/paper/2204.02445/) (2022)
- [What's in your hands? 3D Reconstruction of Generic Objects in Hands](https://mlsanity.com/paper/2204.07153/) (2022)
- [FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling](https://mlsanity.com/paper/2104.08418/) (2021)
- [Amodal Ground Truth and Completion in the Wild](https://mlsanity.com/paper/2312.17247/) (2023)
- [Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion](https://mlsanity.com/paper/2304.10535/) (2023)
- [Text and Image Guided 3D Avatar Generation and Manipulation](https://mlsanity.com/paper/2202.06079/) (2022)
- [Birds of a Feather: Capturing Avian Shape Models from Images](https://mlsanity.com/paper/2105.09396/) (2021)
- [Topologically-Aware Deformation Fields for Single-View 3D Reconstruction](https://mlsanity.com/paper/2205.06267/) (2022)
- [Differentiable Stereopsis: Meshes from multiple views using differentiable rendering](https://mlsanity.com/paper/2110.05472/) (2021)
- [SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators](https://mlsanity.com/paper/2201.00112/) (2022)
- [Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency](https://mlsanity.com/paper/2204.10310/) (2022)
- [Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos](https://mlsanity.com/paper/2403.17103/) (2024)
- [Real3D: Scaling Up Large Reconstruction Models with Real-World Images](https://mlsanity.com/paper/2406.08479/) (2024)
- [To The Point: Correspondence-driven monocular 3D category reconstruction](https://mlsanity.com/paper/2106.05662/) (2021)
- [Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild](https://mlsanity.com/paper/2101.06860/) (2021)
- [FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction](https://mlsanity.com/paper/2205.07763/) (2022)
- [Bayesian Diffusion Models for 3D Shape Reconstruction](https://mlsanity.com/paper/2403.06973/) (2024)
- [Learning monocular 3D reconstruction of articulated categories from motion](https://mlsanity.com/paper/2103.16352/) (2021)
- [DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field](https://mlsanity.com/paper/2308.08231/) (2023)
- [Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction](https://mlsanity.com/paper/2104.00858/) (2021)
- [SAOR: Single-View Articulated Object Reconstruction](https://mlsanity.com/paper/2303.13514/) (2023)
- [StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects](https://mlsanity.com/paper/2105.08016/) (2021)
- [Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields](https://mlsanity.com/paper/2503.20776/) (2025)
- [3D Reconstruction of Objects in Hands without Real World 3D Supervision](https://mlsanity.com/paper/2305.03036/) (2023)

## Abstract (arXiv)

Object reconstruction from a single image -- in the wild -- is a problem where we can make progress and get meaningful results today. This is the main message of this paper, which introduces an automated pipeline with pixels as inputs and 3D surfaces of various rigid categories as outputs in images of realistic scenes. At the core of our approach are deformable 3D models that can be learned from 2D annotations available in existing object detection datasets, that can be driven by noisy automatic object segmentations and which we complement with a bottom-up module for recovering high-frequency shape details. We perform a comprehensive quantitative analysis and ablation study of our approach using the recently introduced PASCAL 3D+ dataset and show very encouraging automatic reconstructions on PASCAL VOC.