# SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images

arXiv: https://arxiv.org/abs/2501.04689 · HTML: https://mlsanity.com/paper/2501.04689/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 76 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 30 of 66)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [MVDream: Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2308.16512/) (2023)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://mlsanity.com/paper/2402.05054/) (2024)
- [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://mlsanity.com/paper/2212.08751/) (2022)
- [Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction](https://mlsanity.com/paper/2109.00512/) (2021)
- [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018)
- [SyncDreamer: Generating Multiview-consistent Images from a Single-view Image](https://mlsanity.com/paper/2309.03453/) (2023)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation](https://mlsanity.com/paper/2212.00774/) (2022)
- [3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction](https://mlsanity.com/paper/1604.00449/) (2016)
- [Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model](https://mlsanity.com/paper/2310.15110/) (2023)
- [PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows](https://mlsanity.com/paper/1906.12320/) (2019)
- [Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis](https://mlsanity.com/paper/2111.04276/) (2021)
- [GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images](https://mlsanity.com/paper/2209.11163/) (2022)
- [InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models](https://mlsanity.com/paper/2404.07191/) (2024)
- [Shap-E: Generating Conditional 3D Implicit Functions](https://mlsanity.com/paper/2305.02463/) (2023)
- [GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting](https://mlsanity.com/paper/2404.19702/) (2024)

## Built on by (most cited first, 30 of 42)

- [DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning](https://mlsanity.com/paper/2503.15265/) (2025)
- [ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding](https://mlsanity.com/paper/2506.01853/) (2025)
- [Unleashing Vecset Diffusion Model for Fast Shape Generation](https://mlsanity.com/paper/2503.16302/) (2025)
- [MV-SAM3D: Adaptive Multi-View Fusion for Layout-Aware 3D Generation](https://mlsanity.com/paper/2603.11633/) (2026)
- [HY3D-Bench: Generation of 3D Assets](https://mlsanity.com/paper/2602.03907/) (2026)
- [CoRe3D: Collaborative Reasoning as a Foundation for 3D Intelligence](https://mlsanity.com/paper/2512.12768/) (2025)
- [3D-RE-GEN: 3D Reconstruction of Indoor Scenes with a Generative Framework](https://mlsanity.com/paper/2512.17459/) (2025)
- [LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning](https://mlsanity.com/paper/2504.18424/) (2025)
- [3D Arena: An Open Platform for Generative 3D Evaluation](https://mlsanity.com/paper/2506.18787/) (2025)
- [Topology-Preserved Auto-regressive Mesh Generation in the Manner of Weaving Silk](https://mlsanity.com/paper/2507.02477/) (2025)
- [SViM3D: Stable Video Material Diffusion for Single Image 3D Generation](https://mlsanity.com/paper/2510.08271/) (2025)
- [NOVA3R: Non-pixel-aligned Visual Transformer for Amodal 3D Reconstruction](https://mlsanity.com/paper/2603.04179/) (2026)
- [Stream3D: Sequential Multi-View 3D Generation via Evidential Memory](https://mlsanity.com/paper/2605.21472/) (2026)
- [Points-to-3D: Structure-Aware 3D Generation with Point Cloud Priors](https://mlsanity.com/paper/2603.18782/) (2026)
- [DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation](https://mlsanity.com/paper/2504.11347/) (2025)
- [Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity](https://mlsanity.com/paper/2508.05609/) (2025)
- [Voxify3D: Pixel Art Meets Volumetric Rendering](https://mlsanity.com/paper/2512.07834/) (2025)
- [Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with Progressive Texture Infilling](https://mlsanity.com/paper/2510.23605/) (2025)
- [HiFi-Mesh: High-Fidelity Efficient 3D Mesh Generation via Compact Autoregressive Dependence](https://mlsanity.com/paper/2601.21314/) (2026)
- [AssetGen: Deployable 3D Asset Generation at Interactive Speed](https://mlsanity.com/paper/2605.26137/) (2026)
- [ELSA3D: Elastic Semantic Anchoring for Unified 3D Understanding and Generation](https://mlsanity.com/paper/2607.06565/) (2026)
- [T-Rex: Task-Adaptive Spatial Representation Extraction for Robotic Manipulation with Vision-Language Models](https://mlsanity.com/paper/2506.19498/) (2025)
- [Benefit from Reference: Retrieval-Augmented Cross-modal Point Cloud Completion](https://mlsanity.com/paper/2507.14485/) (2025)
- [VENI: Variational Encoder for Natural Illumination](https://mlsanity.com/paper/2601.14079/) (2026)
- [GeoFusionLRM: Geometry-Aware Self-Correction for Consistent 3D Reconstruction](https://mlsanity.com/paper/2602.14119/) (2026)
- [ReLi3D: Relightable Multi-view 3D Reconstruction with Disentangled Illumination](https://mlsanity.com/paper/2603.19753/) (2026)
- [AntiGrounding: Executable Robot Trajectories as Visual Prompts for VLM-Guided Manipulation](https://mlsanity.com/paper/2506.12374/) (2025)
- [DATR: Diffusion-based 3D Apple Tree Reconstruction Framework with Sparse-View](https://mlsanity.com/paper/2508.19508/) (2025)
- [AI as intermediary in modern-day ritual: An immersive, interactive production of the roller disco musical Xanadu at UCLA](https://mlsanity.com/paper/2511.06195/) (2025)
- [SPAGS: Sparse-View Articulated Object Reconstruction from Single State via Planar Gaussian Splatting](https://mlsanity.com/paper/2511.17092/) (2025)

## Abstract (arXiv)

We study the problem of single-image 3D object reconstruction. Recent works have diverged into two directions: regression-based modeling and generative modeling. Regression methods efficiently infer visible surfaces, but struggle with occluded regions. Generative methods handle uncertain regions better by modeling distributions, but are computationally expensive and the generation is often misaligned with visible surfaces. In this paper, we present SPAR3D, a novel two-stage approach aiming to take the best of both directions. The first stage of SPAR3D generates sparse 3D point clouds using a lightweight point diffusion model, which has a fast sampling speed. The second stage uses both the sampled point cloud and the input image to create highly detailed meshes. Our two-stage design enables probabilistic modeling of the ill-posed single-image 3D task while maintaining high computational efficiency and great output fidelity. Using point clouds as an intermediate representation further allows for interactive user edits. Evaluated on diverse datasets, SPAR3D demonstrates superior performance over previous state-of-the-art methods, at an inference speed of 0.7 seconds. Project page with code and model: https://spar3d.github.io