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Development of building segmentation approaches from a given LiDAR sceneAuthor: Gaurav Parida Date: 2018-07-31 Report no: IIIT/TH/2018/63 Advisor:K S Rajan AbstractAbstract LiDAR has been emerging as one of the most sought after technologies with it’s novelty to perceive 3D environments accurately. One of the popular application of the lidar data has been object segmen- tation of different objects from a given scene. Lidar as a data format can record the 3D structure of the buildings in urban areas with precise object information. Such platforms to record lidar are mostly clas- sified into three types that is terrestrial, aerial and mobile based lidar systems. The data being recorded from respective platforms varies a lot in terms of range, coverage and point density. Our work presents new approaches to segment buildings from a given lidar scene with flexibilty to work with lidar data from multiple different platforms. The challenge of using LiDAR datasets is the very high resolution of datasets, with high computa- tional requirements in visualizing and processing the datasets. The current methods of object segmen- tation and extraction and classification of aerial LiDAR data are manual and tedious. Our work tries at modelling this problem into a generic pipeline which can be used to segment different varieties of lidar datasets. The pipeline constructed is a result of multiple geometric approaches taken to segment the buildings. The massiveness of data to process, the accuracy of groundline estimation, detection of connected roofplanes and occlusion and overlap of multiple objects in a given lidar scene poses a range of challenges while processing and segmenting such data. Our work tries to overcome some of the challenges and shortcomings of the existing works by proposing two new methods of segmenting objects. Objects in this thesis refer to only buildings. The justification of segmenting only buildings and not focusing on any other type of objects is primarily due to the uniformity in shape of the building and its adherance to geometric properties. In case of other objects, such advantages are not present making their segmentation more challenging and difficult to formulate. The scope of the thesis is limited to only aerial LiDAR because of it’s larger coverage and more processing friendly density compared to terrestrial scans. Terrestrial LiDAR can not capture the roof planes of the buildings while in case of aerial LiDAR we can capture the roof planes of the buildings, which are later used to segment buildings from the given scene. Existing methods of building segmentation from a LiDAR scene mostly involves the usage of image processing based techniques like morphological operations and fitting done in a plane through RANSAC algorithm. These methods often are highly dependent on the resolution of the input data being fed into it. Some methods also take the help of other data to aid in segmentation. Our work tries to tackle the shortcomings of the above works and present a new and novel method to segment buildings from a given scene. We propose two different approaches in segmenting buildings from a given scene. The commonality of the different approaches is that both of them focus on using the facade planes of the buildings to segment them. The algorithm has been tested in multiple datasets ranging from synthetic and real-world datasets. A bottom-up geometric rule-based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. Multiple lidar datasets were used to check the accuracy and performance of the algorithm. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real-world data with an accuracy of 68%. Our work tries to solve a part of the puzzle by segmenting only the buildings in a given lidar scene automatically with limited tuning, in an intuitive manner. The further advantage of the proposed work is its non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focusing on the roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is the crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. It is hoped that such information of buildings in urban landscapes, may help in urban planning and the smart cities endeavour Full thesis: pdf Centre for Others |
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