Learning Scene Dynamics from Point Cloud Sequences

Towards 4d Virtual City Reconstruction from Lidar Point Cloud Sequences

In this paper we propose a joint approach on virtual city reconstruction and dynamic scene analysis based on point cloud sequences of a single car-mounted Rotating Multi-Beam (RMB) Lidar sensor. The aim of the addressed work is to create 4D spatio-temporal models of large dynamic urban scenes containing various moving and static objects. Standalone RMB Lidar devices have been frequently applied...

Learning 3D Point Cloud Histograms

In this paper we show how using histograms based on the angular relationships between a subset of point normals in a 3D point Cloud can be used in a machine learning algorithm in order to recognize different classes of objects given by their 3D point clouds. This approach extends the work done by Gary Bradski at Willow Garage on point clouds recognition by applying a machine learning approach t...

Object Recognition in 3D Point Cloud of Urban Street Scene

In this paper we present a novel street scene semantic recognition framework, which takes advantage of 3D point clouds captured by a high-definition LiDAR laser scanner. An important problem in object recognition is the need for sufficient labeled training data to learn robust classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by reduction...

Cluster-Based Point Cloud Analysis for Rapid Scene Interpretation

A histogram-based method for the interpretation of three-dimensional (3D) point clouds is introduced, where point clouds represent the surface of a scene of multiple objects and background. The proposed approach relies on a pose-invariant object representation that describes the distribution of surface point-pair relations as a model histogram. The models of the used objects are previously trai...

What Does the Scene Look Like from a Scene Point?