Core Concepts
Hash3D, a versatile and training-free acceleration method, leverages feature redundancy in diffusion-based 3D generation to substantially reduce computational costs without compromising visual quality.
Abstract
The paper introduces Hash3D, a novel approach to accelerate diffusion-based 3D generation models without any additional training. The key insight is that feature maps rendered from nearby camera positions and diffusion time-steps exhibit a high degree of redundancy.
Hash3D employs an adaptive grid-based hashing mechanism to efficiently store and retrieve these similar features, significantly reducing the number of redundant calculations required during the optimization process. This feature-sharing strategy not only speeds up the 3D generation but also enhances the smoothness and view consistency of the synthesized 3D objects.
The authors extensively evaluate Hash3D by integrating it with a diverse range of text-to-3D and image-to-3D models. The results demonstrate that Hash3D can accelerate the optimization process by 1.3 to 4 times without compromising performance. Additionally, the integration of Hash3D with 3D Gaussian Splatting leads to a substantial reduction in processing time, cutting down text-to-3D to about 10 minutes and image-to-3D to roughly 30 seconds.
The paper highlights the following key contributions:
Introduction of the versatile, plug-and-play, and training-free Hash3D method to accelerate diffusion-based 3D generation.
Identification of the redundancy in diffusion models when processing nearby views and timesteps, which motivates the development of Hash3D.
Adaptive grid-based hashing to efficiently retrieve features, significantly reducing computations across views and time.
Extensive evaluation across a range of text-to-3D and image-to-3D models, demonstrating 1.3 to 4 times speedup without compromising quality.
Stats
The paper does not provide any specific numerical data or statistics to support the key claims. The focus is on the conceptual and architectural aspects of the proposed Hash3D method.
Quotes
The paper does not contain any direct quotes that are crucial to the key arguments.