The process of 3D scene reconstruction can be affected by numerous uncertainty sources in
real-world scenes. While Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (GS) …

Diffusion and flow models have achieved remarkable successes in various applications
such as text-to-image generation. However, these models typically rely on the same …

We present DGGS, a novel framework addressing the previously unexplored challenge of
Distractor-free Generalizable 3D Gaussian Splatting (3DGS). It accomplishes two key …

Synthesizing photo-realistic visual observations from an ego vehicle's driving trajectory is a
critical step towards scalable training of self-driving models. Reconstruction-based methods …

There has been extensive progress in the reconstruction and generation of 4D scenes from
monocular casually-captured video. While these tasks rely heavily on known camera poses …

We propose a novel framework to remove transient objects from input videos for 3D scene
reconstruction using Gaussian Splatting. Our framework consists of the following steps. In …

Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS) in scenes
featuring transient objects is challenging. We propose a novel hybrid representation, termed …

We propose a novel framework for scene decomposition and static background
reconstruction from everyday videos. By integrating the trained motion masks and modeling …

3D Gaussian Splatting (3DGS) allows flexible adjustments to scene representation, enabling
continuous optimization of scene quality during dense visual simultaneous localization and …