Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission

• URL: http://arxiv.org/abs/2601.01210v1
• Date: Sat, 03 Jan 2026 15:27:57 GMT
• Title: Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission
• Authors: Kazuhiko Murasaki, Shunsuke Konagai, Masakatsu Aoki, Taiga Yoshida, Ryuichi Tanida,
• Abstract summary: This paper presents a high-speed LiDAR point cloud densification method to generate dense 3D scene with minimal latency.<n>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.<n>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.
• Score: 0.6306978246081342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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