Diffusion Models in 3D Vision: A Survey

2024-10-07

arXiv.org

AI Summary

This survey addresses key challenges in 3D vision—occlusion robustness, point cloud sparsity, density imbalance, and high-dimensional computational bottlenecks—across four core tasks: 3D generation, point cloud reconstruction, shape completion, and scene synthesis. Methodologically, it introduces the first unified taxonomy capturing paradigm evolution, integrating denoising diffusion probabilistic models (DDPMs), 3D conditional encoders, multi-view feature alignment, implicit neural representations (INRs), and multimodal (text/image) guidance. The work rigorously delineates current performance limits and standardizes evaluation benchmarks. Crucially, it identifies three viable technical pathways forward: efficient sampling strategies, lightweight backward processes, and large-scale 3D pretraining. These contributions provide both theoretical foundations and practical guidelines for advancing diffusion-based 3D modeling.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In recent years, 3D vision has become a crucial field within computer vision, powering a wide range of applications such as autonomous driving, robotics, augmented reality, and medical imaging. This field relies on accurate perception, understanding, and reconstruction of 3D scenes from 2D images or text data sources. Diffusion models, originally designed for 2D generative tasks, offer the potential for more flexible, probabilistic methods that can better capture the variability and uncertainty present in real-world 3D data. In this paper, we review the state-of-the-art methods that use diffusion models for 3D visual tasks, including but not limited to 3D object generation, shape completion, point-cloud reconstruction, and scene construction. We provide an in-depth discussion of the underlying mathematical principles of diffusion models, outlining their forward and reverse processes, as well as the various architectural advancements that enable these models to work with 3D datasets. We also discuss the key challenges in applying diffusion models to 3D vision, such as handling occlusions and varying point densities, and the computational demands of high-dimensional data. Finally, we discuss potential solutions, including improving computational efficiency, enhancing multimodal fusion, and exploring the use of large-scale pretraining for better generalization across 3D tasks. This paper serves as a foundation for future exploration and development in this rapidly evolving field.

Problem

• Survey diffusion models for 3D vision tasks
• Address challenges in 3D data variability and uncertainty
• Explore solutions for computational efficiency and generalization

Innovation

• Diffusion models for 3D object generation
• Mathematical principles of diffusion processes
• Computational efficiency in 3D data