4 accepted papers
Feed-forward 3D Gaussian Splatting (3DGS) enables efficient one-pass scene reconstruction, providing 3D representations for novel view synthesis without per-scene optimization. However, existing methods typically predict pixel-aligned primitives per-view, producing an excessive number of primitives
We present MoBGS, a novel motion deblurring 3D Gaussian Splatting (3DGS) framework capable of reconstructing sharp and high-quality novel spatio-temporal views from blurry monocular videos in an end-to-end manner. Existing dynamic novel view synthesis (NVS) methods are highly sensitive to motion blu
We present an implicit video representation for occlusions, appearance, and motion disentanglement from monocular videos, which we refer to as Video Spatiotemporal Splines (VideoSPatS).Unlike previous methods that map time and coordinates to deformation and canonical colors, our VideoSPatS maps inpu…
In this paper, we propose a self-supervised single-view pixel-level accurate depth estimation network, called PLADE-Net. The PLADE-Net is the first work that shows unprecedented accuracy levels, exceeding 95% in terms of the \delta^1 metric on the challenging KITTI dataset. Our PLADE-Net is based on…