Sparis: Neural Implicit Surface Reconstruction of Indoor Scenes from Sparse Views
This work addresses the challenge of reconstructing indoor scenes from sparse views, which is incremental as it builds on neural implicit surface models by improving priors for better accuracy.
The paper tackles the problem of indoor scene geometry reconstruction from sparse multi-view images, where existing methods fail due to scale ambiguity and performance deterioration with limited views, and proposes Sparis, which introduces a novel prior based on inter-image matching to achieve superior performance on benchmarks.
In recent years, reconstructing indoor scene geometry from multi-view images has achieved encouraging accomplishments. Current methods incorporate monocular priors into neural implicit surface models to achieve high-quality reconstructions. However, these methods require hundreds of images for scene reconstruction. When only a limited number of views are available as input, the performance of monocular priors deteriorates due to scale ambiguity, leading to the collapse of the reconstructed scene geometry. In this paper, we propose a new method, named Sparis, for indoor surface reconstruction from sparse views. Specifically, we investigate the impact of monocular priors on sparse scene reconstruction, introducing a novel prior based on inter-image matching information. Our prior offers more accurate depth information while ensuring cross-view matching consistency. Additionally, we employ an angular filter strategy and an epipolar matching weight function, aiming to reduce errors due to view matching inaccuracies, thereby refining the inter-image prior for improved reconstruction accuracy. The experiments conducted on widely used benchmarks demonstrate superior performance in sparse-view scene reconstruction.