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

本研究解决了单幅图像三维物体重建中的问题，针对现有方法在可见和遮挡区域重建效果上的不足，提出了一种新颖的两阶段方法SPAR3D。该方法结合稀疏三维点云生成和细致网格构建，既提高了计算效率，又保持了高保真度，其在多种数据集上的评估结果超越了现有的最先进技术，且推理速度为0.7秒。

SPAR3D：基于单幅图像的稳定点感知三维物体重建