Self-augmented Gaussian Splatting with Structure-aware Masks for Sparse-view 3D Reconstruction

• URL: http://arxiv.org/abs/2408.04831v2
• Date: Wed, 14 Aug 2024 05:20:44 GMT
• Title: Self-augmented Gaussian Splatting with Structure-aware Masks for Sparse-view 3D Reconstruction
• Authors: Lingbei Meng, Bi'an Du, Wei Hu,
• Abstract summary: Sparse-view 3D reconstruction is a formidable challenge in computer vision. We propose a self-augmented coarse-to-fine Gaussian splatting paradigm, enhanced with a structure-aware mask. Our method achieves state-of-the-art performances for sparse input views in both perceptual quality and efficiency.
• Score: 9.953394373473621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse-view 3D reconstruction stands as a formidable challenge in computer vision, aiming to build complete three-dimensional models from a limited array of viewing perspectives. This task confronts several difficulties: 1) the limited number of input images that lack consistent information; 2) dependence on the quality of input images; and 3) the substantial size of model parameters. To address these challenges, we propose a self-augmented coarse-to-fine Gaussian splatting paradigm, enhanced with a structure-aware mask, for sparse-view 3D reconstruction. In particular, our method initially employs a coarse Gaussian model to obtain a basic 3D representation from sparse-view inputs. Subsequently, we develop a fine Gaussian network to enhance consistent and detailed representation of the output with both 3D geometry augmentation and perceptual view augmentation. During training, we design a structure-aware masking strategy to further improve the model's robustness against sparse inputs and noise.Experimental results on the MipNeRF360 and OmniObject3D datasets demonstrate that the proposed method achieves state-of-the-art performances for sparse input views in both perceptual quality and efficiency.

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