Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis

• URL: http://arxiv.org/abs/2304.05218v2
• Date: Tue, 20 Feb 2024 02:23:46 GMT
• Title: Improving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis
• Authors: Shu Chen, Junyao Li, Yang Zhang, and Beiji Zou
• Abstract summary: We propose SfMNeRF, a method to better synthesize novel views as well as reconstruct the 3D-scene geometry. SfMNeRF employs the epipolar, photometric consistency, depth smoothness, and position-of-matches constraints to explicitly reconstruct the 3D-scene structure. Experiments on two public datasets demonstrate that SfMNeRF surpasses state-of-the-art approaches.
• Score: 12.3338393483795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With dense inputs, Neural Radiance Fields (NeRF) is able to render photo-realistic novel views under static conditions. Although the synthesis quality is excellent, existing NeRF-based methods fail to obtain moderate three-dimensional (3D) structures. The novel view synthesis quality drops dramatically given sparse input due to the implicitly reconstructed inaccurate 3D-scene structure. We propose SfMNeRF, a method to better synthesize novel views as well as reconstruct the 3D-scene geometry. SfMNeRF leverages the knowledge from the self-supervised depth estimation methods to constrain the 3D-scene geometry during view synthesis training. Specifically, SfMNeRF employs the epipolar, photometric consistency, depth smoothness, and position-of-matches constraints to explicitly reconstruct the 3D-scene structure. Through these explicit constraints and the implicit constraint from NeRF, our method improves the view synthesis as well as the 3D-scene geometry performance of NeRF at the same time. In addition, SfMNeRF synthesizes novel sub-pixels in which the ground truth is obtained by image interpolation. This strategy enables SfMNeRF to include more samples to improve generalization performance. Experiments on two public datasets demonstrate that SfMNeRF surpasses state-of-the-art approaches. Code is available at https://github.com/XTU-PR-LAB/SfMNeRF

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