3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement

• URL: http://arxiv.org/abs/2412.18565v1
• Date: Tue, 24 Dec 2024 17:36:34 GMT
• Title: 3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement
• Authors: Yihang Luo, Shangchen Zhou, Yushi Lan, Xingang Pan, Chen Change Loy,
• Abstract summary: We present 3DEnhancer, which employs a multi-view latent diffusion model to enhance coarse 3D inputs while preserving multi-view consistency. Unlike existing video-based approaches, our model supports seamless multi-view enhancement with improved coherence across diverse viewing angles.
• Score: 66.8116563135326
• License:
• Abstract: Despite advances in neural rendering, due to the scarcity of high-quality 3D datasets and the inherent limitations of multi-view diffusion models, view synthesis and 3D model generation are restricted to low resolutions with suboptimal multi-view consistency. In this study, we present a novel 3D enhancement pipeline, dubbed 3DEnhancer, which employs a multi-view latent diffusion model to enhance coarse 3D inputs while preserving multi-view consistency. Our method includes a pose-aware encoder and a diffusion-based denoiser to refine low-quality multi-view images, along with data augmentation and a multi-view attention module with epipolar aggregation to maintain consistent, high-quality 3D outputs across views. Unlike existing video-based approaches, our model supports seamless multi-view enhancement with improved coherence across diverse viewing angles. Extensive evaluations show that 3DEnhancer significantly outperforms existing methods, boosting both multi-view enhancement and per-instance 3D optimization tasks.

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