The goal of our work is to generate high-quality novel views from monocular videos of
complex and dynamic scenes. Prior methods, such as DynamicNeRF, have shown …

Recently, attempts to learn the underlying 3D structures of a scene from monocular videos in
a fully self-supervised fashion have drawn much attention. One of the most challenging …

Despite advancements in self-supervised monocular depth estimation, challenges persist in
dynamic scenarios due to the dependence on assumptions about a static world. In this …

Currently, learning-based multi-view stereo (MVS) has been dominated by the pipeline of
3D cost volume and regularization network over the static cost volume for depth regression …

Multi-view stereo (MVS) aims to reconstruct the dense 3D geometry of a scene by
processing and relating images captured from different viewpoints. Despite impressive …

Despite the promising performance achieved by the learning-based multi-view stereo (MVS)
methods, the commonly used feature extractors still struggle with the perspective …

Despite the remarkable success of monocular depth estimation, most works focus on ideal
experiment conditions, such as favorable weather, where there is few environmental factors …

In the domain of multi-baseline stereo, the conventional understanding is that, in general,
increasing baseline separation substantially enhances the accuracy of depth estimation …

The novel view synthesis from a limited set of images is a significant research focus.
Traditional NeRF methods, relying mainly on color supervision, struggle with accurate scene …

Self-supervised monocular depth estimation leverages geometric constraints to create
pseudolabels, eliminating the need for ground-truth data during training. In the field of 3D …