Diffusion Models in 3D Vision: A Survey (2410.04738v3)

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.

• The paper surveys state-of-the-art diffusion models applied to various 3D vision tasks, including object generation, shape completion, and novel view synthesis.
• It details architectural innovations enabling diffusion models to handle 3D data effectively and discusses challenges like computational demands and solutions such as optimization and multimodal fusion.
• The survey reviews key applications, relevant 3D datasets, evaluation metrics like Chamfer Distance and Earth Mover's Distance, and highlights future research directions in the field.

The paper "Diffusion Models in 3D Vision: A Survey" provides an extensive review of the application of diffusion models within the domain of 3D vision. Recently, 3D vision has become instrumental in various sectors such as autonomous driving, robotics, augmented reality, and medical imaging, necessitating accurate interpretation and reconstruction of 3D scenes from 2D inputs. The survey dissects how diffusion models, traditionally used in 2D tasks, have been adopted for 3D tasks to offer more flexible and probabilistic frameworks capable of handling real-world data variability and uncertainty.

Core Contributions:

• State-of-the-Art Diffusion Models: This survey categorizes and summarizes state-of-the-art approaches leveraging diffusion models for a range of 3D visual tasks, including 3D object generation, shape completion, and point cloud reconstruction. It outlines the mathematical constructs underpinning diffusion models, such as the forward diffusion process and its reverse counterpart, which integrates denoising to synthesize structured data from noise.
• Architectural Innovations: The paper discusses architectural advancements enabling diffusion models to effectively engage with 3D datasets. Each variation is methodically analyzed, from Denoising Diffusion Probabilistic Models (DDPMs) to Stochastic Differential Equations (SDEs), exploring their unique strengths and limitations in processing high-dimensional data.
• Challenges and Solutions: Addressing challenges like handling occlusions, varying point densities, and computational demands, the survey highlights potential solutions. These include enhancing computational efficiency through optimization of diffusion steps, leveraging multimodal fusion to integrate various data types (2D, 3D, textual), and large-scale pretraining to improve model robustness and generalization.

Diffusion Model Fundamentals:

The survey covers the theoretical underpinnings of diffusion models, defining them as generative models that create data by reversing a noise-adding process. The forward and reverse processes are detailed mathematically, with the reverse process crucial for learning score functions that estimate gradients of data's log-probability.

Applications in 3D Vision:

1. 3D Object Generation: Diffusion models facilitate high-fidelity 3D shape synthesis, contrasting GANs and VAEs with their ability to produce diverse, high-quality outputs without mode collapse.
2. Novel View Synthesis and Scene Generation: Diffusion models play a crucial role in determining how scenes appear from novel viewpoints, thus proving invaluable for applications that involve interactive media and virtual reality.
3. Text to 3D and Image to 3D: These tasks are highlighted as exploiting the ability of diffusion models to bridge language and 3D structure, facilitating the creation of semantically coherent 3D models from textual descriptions or 2D image inputs.

Datasets and Metrics:

The paper reviews numerous 3D datasets, categorized into object-based, human-based, and scene-based, that serve as benchmarks for evaluating diffusion models in 3D vision. It also summarizes various evaluation metrics such as Chamfer Distance (CD), Earth Mover's Distance (EMD), and Frechet Inception Distance (FID), providing a comprehensive overview of how generative quality and fidelity are assessed in 3D tasks.

Future Directions:

The paper points out key areas for future research, such as improving inference speed, scaling diffusion models to handle complex, dynamic scenes, and incorporating physical constraints for realistic content generation. These enhancements could make diffusion models more applicable to real-world 3D vision tasks, such as robotics and simulation, where precision and efficiency are critical.

In summary, the survey emphasizes the transformative potential of diffusion models in progressing 3D vision, offering insights and guidance for expanding their application and optimization within this domain.