Recognizing point clouds using conditional random fields

Detecting objects in cluttered scenes is a necessary step for many robotic tasks and facilitates the interaction of the robot with its environment. Because of the availability of efficient 3D sensing devices as the Kinect, methods for the recognition of objects in 3D point clouds have gained importance during the last years. In this paper, we propose a new supervised learning approach for the recognition of objects from 3D point clouds using Conditional Random Fields, a type of discriminative, undirected probabilistic graphical model. The various features and contextual relations of the objects are described by the potential functions in the graph. Our method allows for learning and inference from unorganized point clouds of arbitrary sizes and shows significant benefit in terms of computational speed during prediction when compared to a state-of-the-art approach based on constrained optimization.Peer ReviewedPostprint (author’s final draft

DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation

We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with over a half-billion hand-labeled points spanning 10 square kilometers of area and eight object categories. Large annotated point cloud data sets have become the standard for evaluating deep learning methods. However, most of the existing data sets focus on data collected from a mobile or terrestrial scanner with few focusing on aerial data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a new set of challenges and applications in areas such as 3D urban modeling and large-scale surveillance. DALES is the most extensive publicly available ALS data set with over 400 times the number of points and six times the resolution of other currently available annotated aerial point cloud data sets. This data set gives a critical number of expert verified hand-labeled points for the evaluation of new 3D deep learning algorithms, helping to expand the focus of current algorithms to aerial data. We describe the nature of our data, annotation workflow, and provide a benchmark of current state-of-the-art algorithm performance on the DALES data set.Comment: 8 page

Sensor fusion for semantic segmentation of urban scenes

Abstract—Semantic understanding of environments is an important problem in robotics in general and intelligent au-tonomous systems in particular. In this paper, we propose a semantic segmentation algorithm which effectively fuses infor-mation from images and 3D point clouds. The proposed method incorporates information from multiple scales in an intuitive and effective manner. A late-fusion architecture is proposed to maximally leverage the training data in each modality. Finally, a pairwise Conditional Random Field (CRF) is used as a post-processing step to enforce spatial consistency in the structured prediction. The proposed algorithm is evaluated on the publicly available KITTI dataset [1] [2], augmented with additional pixel and point-wise semantic labels for building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence regions. A per-pixel accuracy of 89.3 % and average class accuracy of 65.4 % is achieved, well above current state-of-the-art [3]. I

A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION

The lack of benchmarking data for the semantic segmentation of digital heritage scenarios is hampering the development of automatic classification solutions in this field. Heritage 3D data feature complex structures and uncommon classes that prevent the simple deployment of available methods developed in other fields and for other types of data. The semantic classification of heritage 3D data would support the community in better understanding and analysing digital twins, facilitate restoration and conservation work, etc. In this paper, we present the first benchmark with millions of manually labelled 3D points belonging to heritage scenarios, realised to facilitate the development, training, testing and evaluation of machine and deep learning methods and algorithms in the heritage field. The proposed benchmark, available at http://archdataset.polito.it/, comprises datasets and classification results for better comparisons and insights into the strengths and weaknesses of different machine and deep learning approaches for heritage point cloud semantic segmentation, in addition to promoting a form of crowdsourcing to enrich the already annotated databas

功能性规则约束下的三维点云道路设施语义标注

三维场景的语义标注研究是机器视觉、摄影测量以及机器学习等领域的热门研究课题.但基于移动激光扫描数据的道路设施精确解释仍处于瓶颈期.提出一种基于逻辑关系和功能性对道路设施进行语义标注的新方法,先总结制定道路设施相关的特征符号和规则,再根据所定义的规则功能对点云数据进行语义标注.基于该方法对国内某中等城市道路点云数据进行了相当详尽的解释,正确提取了93%的杆状物体,并全部正确识别.对于杆状物体的附件(如灯头、交通标志等),基本正确识别且有效标记.与改进的RANSAC算法相比,该方法提供了一个较好的解决方案,有助于在城市环境中自动绘制详细的道路设施.国家自然科学基金(41771439);;国家重点研发计划项目(2016YFB0502300);;江苏省研究生科研与实践创新计划项目(KYCX18_1206