SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single Images (2501.04689v1)

Abstract: 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

• The paper introduces a two-stage framework that integrates point sampling via diffusion with mesh refinement for accurate 3D reconstruction.
• It leverages a lightweight point diffusion model to generate sparse point clouds, effectively addressing occluded regions.
• The method achieves rapid 0.7-second inference, supporting interactive edits and superior performance on diverse datasets.

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

SPAR3D introduces an innovative approach for reconstructing 3D objects from single images by integrating both regression-based modeling and diffusion-based generative modeling. The proposed method addresses limitations associated with each approach independently, effectively leveraging the advantages of both. By employing a two-stage process, SPAR3D efficiently reconstructs precise 3D meshes with high fidelity and computational efficiency.

Key Contributions

SPAR3D's core innovation lies in its two-stage design:

1. Point Sampling Stage: A lightweight point diffusion model generates sparse 3D point clouds from an input image. This model excels by balancing efficient sampling speed with the probabilistic modeling of uncertain 3D reconstructions, particularly useful for occluded regions.
2. Meshing Stage: Using the sampled point clouds and image data, the model refines the point clouds into highly detailed 3D meshes. This stage ensures that the final output is aligned with the visible surfaces of the input image, leveraging local image features to enhance detail and fidelity.

Performance Evaluation

The design efficiently navigates the challenges of single-image 3D reconstruction, demonstrating superior performance over existing state-of-the-art methods on diverse datasets. SPAR3D achieves an inference speed of 0.7 seconds, underscoring its applicability for practical applications where rapid processing is crucial.

Implications and Future Directions

SPAR3D's method of using point clouds as intermediate representations offers enhanced interactivity for user edits. This feature allows users to adjust and refine 3D reconstructions skillfully, potentially broadening the scope for customizability in 3D modeling applications. The model's performance on in-the-wild images also lends credence to its robustness and generalization capabilities, suggesting its viability in various application domains, including augmented reality and visual effects.

Looking ahead, SPAR3D's framework sets a precedent for future exploration in hybrid modeling approaches, encouraging further refinement of both point sampling strategies and mesh refinement processes. Future work may explore the integration of more sophisticated learning algorithms and alternative intermediate representations to further enhance the model's robustness and efficiency.

Conclusion

SPAR3D marks a notable advancement in the field of single-image 3D reconstruction, offering a compelling synthesis of regression and generative modeling paradigms. With its efficient two-stage process and strong performance metrics, SPAR3D is well-positioned to influence future developments in rapid and accurate 3D modeling technologies. The approach's ability to support interactive edits enhances its practical utility, making it a valuable tool for both research and industry applications in computer vision and beyond.