AugGS: Self-augmented Gaussians with Structural Masks for Sparse-view 3D Reconstruction

• URL: http://arxiv.org/abs/2408.04831v4
• Date: Tue, 31 Dec 2024 10:54:55 GMT
• Title: AugGS: Self-augmented Gaussians with Structural Masks for Sparse-view 3D Reconstruction
• Authors: Bi'an Du, Lingbei Meng, Wei Hu,
• Abstract summary: Sparse-view 3D reconstruction is a major challenge in computer vision.<n>We propose a self-augmented two-stage Gaussian splatting framework enhanced with structural masks for sparse-view 3D reconstruction.<n>Our approach achieves state-of-the-art performance in perceptual quality and multi-view consistency with sparse inputs.
• Score: 9.953394373473621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse-view 3D reconstruction is a major challenge in computer vision, aiming to create complete three-dimensional models from limited viewing angles. Key obstacles include: 1) a small number of input images with inconsistent information; 2) dependence on input image quality; and 3) large model parameter sizes. To tackle these issues, we propose a self-augmented two-stage Gaussian splatting framework enhanced with structural masks for sparse-view 3D reconstruction. Initially, our method generates a basic 3D Gaussian representation from sparse inputs and renders multi-view images. We then fine-tune a pre-trained 2D diffusion model to enhance these images, using them as augmented data to further optimize the 3D Gaussians. Additionally, a structural masking strategy during training enhances the model's robustness to sparse inputs and noise. Experiments on benchmarks like MipNeRF360, OmniObject3D, and OpenIllumination demonstrate that our approach achieves state-of-the-art performance in perceptual quality and multi-view consistency with sparse inputs.

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