用实时融合多源数据生成高密度点云，解决沉浸式远程传输延迟问题。
Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission
- 融合多LiDAR与高清彩色图像，通过卷积神经网络实现联合双边滤波
- 30帧/秒实时生成全高清密度深度图，速度超基准方法15倍以上
- 生成点云几何准确无多视角不一致或鬼影伪影，适合低延迟场景应用
为实现沉浸式远程存在感的低延迟空间数据传输系统，面临两大挑战：动态三维场景的密集捕捉与实时处理。激光雷达（LiDAR）虽能实时获取三维信息，但生成稀疏点云。本文提出一种高速激光雷达点云稠密化方法，在极低延迟下生成高密度三维场景，满足实时深度补全需求并保持实时性能。该方法结合多路LiDAR输入与高分辨率彩色图像，采用基于卷积神经网络架构的联合双边滤波策略。实验表明，所提方法可在30帧/秒下生成全高清分辨率的稠密深度图，速度超过最近基于训练的深度补全方法15倍以上。生成的稠密点云几何精度高，无多视角不一致性或鬼影伪影。
原文摘要 · Abstract (English)
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.
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