A Comprehensive Survey on Diffusion Model-Driven 3D Reconstruction: Methods, Datasets, and Prospects

Engineering notes

Key topics: autonomous driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Three-dimensional (3D) reconstruction serves as a key technology bridging the real and digital worlds, with broad application in remote sensing, autonomous driving, and robotics. In recent years, its technical paradigm has shifted from geometry-based methods to data-driven approaches, with diffusion models emerging as a major driving force due to their stable training process, strong ability to learn priors from large-scale datasets, and excellent controllability over outputs. Despite the proliferation of diverse architectures, a systematic analysis and comparison of these methods remains absent. In this paper, we present a comprehensive review of diffusion-based 3D reconstruction methods. Based on the space where diffusion operates, we first categorize existing approaches into four types—image diffusion, latent diffusion, 3D diffusion, and hybrid diffusion—and we provide a detailed analysis of their methodologies. We then summarize commonly used 3D datasets and provide a comparative evaluation of these methods across three dimensions: reconstruction accuracy, computational efficiency, and generalization capabilities. Finally, we discuss future developments of diffusion-based 3D reconstruction methods.

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