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One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization

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arxiv 2306.16928 v1 pith:SCFDKRVK submitted 2023-06-29 cs.CV cs.AIcs.RO

Minghua Liu , Chao Xu , Haian Jin , Linghao Chen , Mukund Varma T , Zexiang Xu , Hao Su This is my paper
classification cs.CVcs.AIcs.RO
keywords imagereconstructionsinglemethoddiffusioninputmeshmethods
verification ladder T0 review T1 audit T2 compute T3 formal
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Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and poor geometry. In this work, we propose a novel method that takes a single image of any object as input and generates a full 360-degree 3D textured mesh in a single feed-forward pass. Given a single image, we first use a view-conditioned 2D diffusion model, Zero123, to generate multi-view images for the input view, and then aim to lift them up to 3D space. Since traditional reconstruction methods struggle with inconsistent multi-view predictions, we build our 3D reconstruction module upon an SDF-based generalizable neural surface reconstruction method and propose several critical training strategies to enable the reconstruction of 360-degree meshes. Without costly optimizations, our method reconstructs 3D shapes in significantly less time than existing methods. Moreover, our method favors better geometry, generates more 3D consistent results, and adheres more closely to the input image. We evaluate our approach on both synthetic data and in-the-wild images and demonstrate its superiority in terms of both mesh quality and runtime. In addition, our approach can seamlessly support the text-to-3D task by integrating with off-the-shelf text-to-image diffusion models.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.

  2. LRM: Large Reconstruction Model for Single Image to 3D

    cs.CV 2023-11 conditional novelty 7.0 of 10

    LRM is a large transformer that predicts a NeRF directly from a single image after training on a million-object multi-view dataset.

  3. DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

    cs.CV 2023-09 unverdicted novelty 7.0 of 10

    DreamGaussian creates high-quality textured 3D meshes from single-view images in 2 minutes via generative Gaussian Splatting with mesh extraction and UV refinement.

  4. Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    Flux-GS is a mobile-optimized 3D Gaussian Splatting method that compresses specular energy via Monte Carlo aggregation, recovers details with attribute-conditioned SH offsets, and uses multi-view guidance for densific...

  5. CLONE: Continuous Latent Optimization for Normal Estimation via 3D Gaussian Splatting

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    CLONE estimates surface normals from images via a differentiable 3D Gaussian splatting loop, using photometric loss instead of normal labels.

  6. BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion

    cs.CV 2024-01 unverdicted novelty 6.0 of 10

    BoostDream refines coarse feed-forward text-to-3D assets via 3D distillation, multi-view SDS loss from a 2D diffusion model, and prompt-consistent normal maps to produce higher-quality results more efficiently than st...

  7. SyncDreamer: Generating Multiview-consistent Images from a Single-view Image

    cs.CV 2023-09 unverdicted novelty 6.0 of 10

    SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention.

  8. Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

    cs.CV 2023-10 unverdicted novelty 5.0 of 10

    Zero123++ produces high-quality 3D-consistent multi-view images from a single input by fine-tuning Stable Diffusion with targeted conditioning and training methods.

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