Graph-based denoising for time-varying point clouds
Noisy 3D point clouds arise in many applications. They may be due to errors when constructing a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information between points. This paper introduces a technique that uses this graph structure and convex optimization methods to denoise 3D point clouds. A short discussion presents how those methods naturally generalize to time-varying inputs such as 3D point cloud time series.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning
The prevalence of accessible depth sensing and 3D laser scanning techniques has enabled the convenient acquisition of 3D dynamic point clouds, which provide efficient representation of arbitrarily-shaped objects in motio…
Denoisinggraph constructionGraph LearningFast graph-based denoising for point cloud color information
Point clouds are utilized in various 3D applications such as cross-reality (XR) and realistic 3D displays. In some applications, e.g., for live streaming using a 3D point cloud, real-time point cloud denoising methods ar…
Denoisinggraph constructionDynamic Point Cloud Denoising via Manifold-to-Manifold Distance
3D dynamic point clouds provide a natural discrete representation of real-world objects or scenes in motion, with a wide range of applications in immersive telepresence, autonomous driving, surveillance, \etc. Neverthele…
Autonomous DrivingDenoisingGraph Learning3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation
We present a neural-network-based architecture for 3D point cloud denoising called neural projection denoising (NPD). In our previous work, we proposed a two-stage denoising algorithm, which first estimates reference pla…
Denoising4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks
Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (e.g., LiDAR in autonomous or assisted driving). In many cases, such volume of data is t…
Edge-computingGraph Attentionpoint cloud upsampling