5 accepted papers
While LiDAR NeRFs excel in static environments, synthesizing dynamic scenes remains challenging as moving objects break multi-view consistency, causing conflicting supervision and ghosting artifacts across frames. Existing methods typically suffer from optimization difficulty from the start, struggl…
Efficient Point Cloud Geometry Compression (PCGC) with a lower bits per point (BPP) and higher peak signal-to-noise ratio (PSNR) is essential for the transportation of large-scale 3D data. Although octree-based entropy models can reduce BPP without introducing geometry distortion, existing CNN-based…
An ideal point cloud registration framework should have superior accuracy, acceptable efficiency, and strong generalizability. However, this is highly challenging since existing registration techniques are either not accurate enough, far from efficient, or generalized poorly. It remains an open ques…
We study the problem of attribute compression for large-scale unstructured 3D point clouds. Through an in-depth exploration of the relationships between different encoding steps and different attribute channels, we introduce a deep compression network, termed 3DAC, to explicitly compress the attribu…
Matching cost aggregation plays a critical role for the stereo matching task. Existing CNN-based methods commonly use 3D convolutions to aggregate matching costs from a local 3D space. However, the high computational cost of 3D convolutions limits their applications. Traditional methods show that a