A self-supervised cyclic neural-analytic approach for novel view synthesis and 3D reconstruction
This work addresses the challenge of generalizing neural rendering to unseen poses and regions for autonomous navigation, representing a strong specific gain rather than a broad breakthrough.
The paper tackles the problem of novel view synthesis and 3D reconstruction for autonomous UAV navigation by proposing a self-supervised cyclic neural-analytic pipeline, resulting in substantial improvements in rendering and reconstruction, especially in undersampled and novel regions.
Generating novel views from recorded videos is crucial for enabling autonomous UAV navigation. Recent advancements in neural rendering have facilitated the rapid development of methods capable of rendering new trajectories. However, these methods often fail to generalize well to regions far from the training data without an optimized flight path, leading to suboptimal reconstructions. We propose a self-supervised cyclic neural-analytic pipeline that combines high-quality neural rendering outputs with precise geometric insights from analytical methods. Our solution improves RGB and mesh reconstructions for novel view synthesis, especially in undersampled areas and regions that are completely different from the training dataset. We use an effective transformer-based architecture for image reconstruction to refine and adapt the synthesis process, enabling effective handling of novel, unseen poses without relying on extensive labeled datasets. Our findings demonstrate substantial improvements in rendering views of novel and also 3D reconstruction, which to the best of our knowledge is a first, setting a new standard for autonomous navigation in complex outdoor environments.