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.

翻译：为实现沉浸式远程呈现的低延迟空间传输系统，存在两大核心挑战：密集捕获动态三维场景并实时处理。LiDAR传感器虽能实时采集三维数据，但生成的点云较为稀疏。为此，本文提出一种高速LiDAR点云增密方法，以最小延迟生成稠密三维场景，在保持实时性能的同时满足动态深度补全需求。该方法融合多源LiDAR输入与高分辨率彩色图像，通过卷积神经网络架构实现联合双边滤波策略。实验表明，所提方法能以实时速率（30帧/秒）生成全高清分辨率的稠密深度图，其速度较近期基于训练的深度补全方法提升超过15倍。所得稠密点云具有精确的几何结构，未出现多视角不一致或重影伪影。