Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909988396466176 |
|---|---|
| author | Murasaki, Kazuhiko Konagai, Shunsuke Aoki, Masakatsu Yoshida, Taiga Tanida, Ryuichi |
| author_facet | Murasaki, Kazuhiko Konagai, Shunsuke Aoki, Masakatsu Yoshida, Taiga Tanida, Ryuichi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01210 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission Murasaki, Kazuhiko Konagai, Shunsuke Aoki, Masakatsu Yoshida, Taiga Tanida, Ryuichi Computer Vision and Pattern Recognition Robotics 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. |
| title | Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2601.01210 |