To realize low-latency spatial transmission system for immersive telepresence, there are two major problems: capturing dynamic 3D scene densely and processing them in real time. LiDAR sensors capture 3D in real time, but produce sparce point clouds. Therefore, this paper presents a high-speed LiDAR point cloud densification method to generate dense 3D scene with minimal latency, addressing the need for on-the-fly depth completion while maintaining real-time performance. Our approach combines multiple LiDAR inputs with high-resolution color images and applies a joint bilateral filtering strategy implemented through a convolutional neural network architecture. Experiments demonstrate that the proposed method produces dense depth maps at full HD resolution in real time (30 fps), which is over 15x faster than a recent training-based depth completion approach. The resulting dense point clouds exhibit accurate geometry without multiview inconsistencies or ghosting artifacts.
@article{arxiv.2601.01210,
title = {Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission},
author = {Kazuhiko Murasaki and Shunsuke Konagai and Masakatsu Aoki and Taiga Yoshida and Ryuichi Tanida},
journal= {arXiv preprint arXiv:2601.01210},
year = {2026}
}