标题： SPAR3D：从单张图像中稳定点感知的3D物体重建 显示英文标题

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

摘要： 我们研究单图像3D物体重建问题。 最近的工作分为两个方向：基于回归的建模和生成建模。 基于回归的方法能高效推断可见表面，但在遮挡区域表现不佳。 生成方法通过建模分布更好地处理不确定区域，但计算成本高，且生成结果常与可见表面不一致。 在本文中，我们提出SPAR3D，一种新颖的两阶段方法，旨在结合两种方向的优势。 SPAR3D的第一阶段使用轻量级点扩散模型生成稀疏的3D点云，具有快速采样速度。 第二阶段利用采样的点云和输入图像生成高度详细的网格。 我们的两阶段设计能够在保持高计算效率和出色输出保真度的同时，对病态的单图像3D任务进行概率建模。 使用点云作为中间表示进一步允许交互式用户编辑。 在多种数据集上评估，SPAR3D在推理速度为0.7秒的情况下，表现出优于先前最先进方法的性能。 项目页面包含代码和模型：https://spar3d.github.io 显示英文摘要

摘要： 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