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This paper offers an extensive review of four pioneering models in the realm of 3D computer vision and generative modeling: GET3D, SDFusion, ATT3D, and GaussianDreamer. Each model is distinguished by its innovative approach to tasks such as depth estimation, 3D reconstruction, point cloud processing, and 3D asset generation. GET3D excels in generating high-quality 3D meshes from 2D images, while SDFusion specializes in 3D shape completion and reconstruction using a latent diffusion model. ATT3D stands out for its attention mechanism that enhances point cloud analysis, and GaussianDreamer uniquely merges 2D and 3D diffusion models for rapid text-to-3D asset creation. Despite their advancements, these models grapple with challenges including computational efficiency, handling complex scenes, and ensuring the realism of generated content. This review delves into their operational principles, the challenges they face, and potential opportunities for future research, aiming to spur further innovation in the field of 3D computer vision and generation.

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Native 3D Diffusion Networks Architectures, Optimization, and Emerging Trends in Generative Modelling

  • 会议论文

  • 发表日期：

    2026年

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  • Shun Fang,
  • Fang Yu

摘要

This paper offers an extensive review of four pioneering models in the realm of 3D computer vision and generative modeling: GET3D, SDFusion, ATT3D, and GaussianDreamer. Each model is distinguished by its innovative approach to tasks such as depth estimation, 3D reconstruction, point cloud processing, and 3D asset generation. GET3D excels in generating high-quality 3D meshes from 2D images, while SDFusion specializes in 3D shape completion and reconstruction using a latent diffusion model. ATT3D stands out for its attention mechanism that enhances point cloud analysis, and GaussianDreamer uniquely merges 2D and 3D diffusion models for rapid text-to-3D asset creation. Despite their advancements, these models grapple with challenges including computational efficiency, handling complex scenes, and ensuring the realism of generated content. This review delves into their operational principles, the challenges they face, and potential opportunities for future research, aiming to spur further innovation in the field of 3D computer vision and generation.