ABSTRACT:3D reconstruction of trees is of great interest in large-scale 3D city modelling. Laser scanners provide geometrically accurate 3D point clouds that are very useful for object recognition in complex urban scenes. Trees often cause important occlusions on building façades. Their recognition can lead to occlusion maps that are useful for many façade oriented applications such as visual based localisation and automatic image tagging. This paper proposes a pipeline to detect trees in point clouds acquired in dense urban areas with only laser informations (x,y, z coordinates and intensity). It is based on local geometric descriptors computed on each laser point using a determined neighbourhood. These descriptors describe the local shape of objects around every 3D laser point. A projection of these values on a 2D horizontal accumulation space followed by a combination of morphological filters provides individual tree clusters. The pipeline is evaluated and the results are presented on a set of one million laser points using a man made ground truth.
Road sign identification in images is an important issue, in particular for vehicle safety applications. It is usually tackled in three stages: detection, recognition and tracking, and evaluated as a whole. To progress towards better algorithms, we focus in this paper on the first stage of the process, namely road sign detection. More specifically, we compare, on the same ground-truth image database, results obtained by three algorithms that sample different state-of-the-art approaches. The three tested algorithms: Contour Fitting, Radial Symmetry Transform, and pair-wise voting scheme, all use color and edge information and are based on geometrical models of road signs. The test dataset is made of 847 images 960 × 1080 of complex urban scenes (available at www.itowns.fr/benchmarking.html). They feature 251 road signs of different shapes (circular, rectangular, triangular), sizes and types. The pros and cons of the three algorithms are discussed, allowing to draw new research perspectives.
Vision based localization is widely investigated for the autonomous navigation and robotics. One of the basic steps of vision based localization is the extraction of interest points in images that are captured by the embedded camera. In this paper, SIFT and SURF extractors were chosen to evaluate their performance in localization. Four street view image sequences captured by a mobile mapping system, were used for the evaluation and both SIFT and SURF were tested on different image scales. Besides, the impact of the interest point distribution was also studied. We evaluated the performances from for aspects: repeatability, precision, accuracy and runtime. The local bundle adjustment method was applied to refine the pose parameters and the 3D coordinates of tie points. According to the results of our experiments, SIFT was more reliable than SURF. Apart from this, both the accuracy and the efficiency of localization can be improved if the distribution of feature points are well constrained for SIFT.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.