Browsing by Author "Martin, Javonne Jason"

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    Creating 3D models using reconstruction techniques
    (Stellenbosch : Stellenbosch University, 2018-12) Martin, Javonne Jason; Kroon, R. S. (Steve); De Villiers, H. A. C.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Computer Science.
    ENGLISH ABSTRACT :Virtual reality models of real world environments have a number of compelling applications, such as preserving the architecture and designs of older buildings. This process can be achieved by using 3D artists to reconstruct the environment, however this is a long and expensive process. Thus, this thesis investigates various techniques and approaches used in 3D reconstruction of environments using a single RGB-D camera and aims to reconstruct the 3D environment to generate a 3D model. This would allow non-technical users to reconstruct environments and use these models in business and simulations, such as selling real-estate, modifying pre-existing structures for renovation and planning. With the recent improvements in virtual reality technology such as the Oculus Rift and HTC Vive, a user can be immersed into virtual reality environments created from real world structures. A system based on Kinect Fusion is implemented to reconstruct an environment and track the motion of the camera within the environment. The system is designed as a series of selfcontained subsystems that allows for each of the subsystems to be modified, expanded upon or easily replaced by alternative methods. The system is made available as an open source C++ project using Nvidia’s CUDA framework to aid reproducibility and provides a platform for future research. The system makes use of the Kinect sensor to capture information about the environment. A coarse-to-fine least squares approach is used to estimate the motion of the camera. In addition, the system employs a frame-to-model approach that uses a view of the estimated reconstruction of the model as the reference frame and the incoming scene data as the target. This minimises the drift with respect to the true trajectory of the camera. The model is built using a volumetric approach, with volumetric information implicitly stored as a truncated signed distance function. The system filters out noise in the raw sensor data by using a bilateral filter. A point cloud is extracted from the volume using an orthogonal ray caster which enables an improved hole-filling approach. This allows the system to extract both the explicit and implicit structure from the volume. The 3D reconstruction is followed by mesh generation based on the point cloud. This is achieved by using an approach related to Delaunay triangulation, the ball-pivot algorithm. The resulting system processes frames at 30Hz, enabling real-time point cloud generation, while the mesh generation occurs offline. This system is initially tested using Blender to generate synthetic data, followed by a series of real world tests. The synthetic data is used to test the presented system’s motion tracking against the ground truth. While the presented system suffers from the effects of drift over long frame sequences, it is shown to be capable of tracking the motion of the camera. This thesis finds that the ball pivot algorithm can generate the edges and faces for synthetic point clouds, however it performs poorly when using the noisy synthetic and real world data sets. Based on the results obtained it is recommended that the obtained point cloud be preprocessed to remove noise before it is provided to the mesh generation algorithm and an alternative mesh generation technique should be employed that is more robust to noise.