Systems Design Engineering

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This is the collection for the University of Waterloo's Department of Systems Design Engineering.

Research outputs are organized by type (eg. Master Thesis, Article, Conference Paper).

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Browsing Systems Design Engineering by Subject "3D Reconstruction"

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    Deep Learning 3D Scans for Footwear Fit Estimation from a Single Depth Map
    (University of Waterloo, 2018-01-02) Lunscher, Nolan
    In clothing and particularly in footwear, the variance in the size and shape of people and of clothing poses a problem of how to match items of clothing to a person. This is specifically important in footwear, as fit is highly dependent on foot shape, which is not fully captured by shoe size. 3D scanning can be used to determine detailed personalized shape information, which can then be used to match against product shape for a more per- sonalized footwear matching experience. In current implementations however, this process is typically expensive and cumbersome. Typical scanning techniques require that a camera capture an object from many views in order to reconstruct shape. This usually requires either many cameras or a moving camera system, both of which being complex engineering tasks to construct. Ideally, in order to reduce the cost and complexity of scanning systems as much as possible, only a single image from a single camera would be needed. With recent techniques, semantics such as knowing the kind of object in view can be leveraged to determine the full 3D shape given incomplete information. Deep learning methods have been shown to be able to reconstruct 3D shape from limited inputs in highly symmetrical objects such as furniture and vehicles. We apply a deep learning approach to the domain of foot scanning, and present meth- ods to reconstruct a 3D point cloud from a single input depth map. Anthropomorphic body parts can be challenging due to their irregular shapes, difficulty for parameterizing and limited symmetries. We present two methods leveraging deep learning models to pro- duce complete foot scans from a single input depth map. We utilize 3D data from MPII Human Shape based on the CAESAR database, and train deep neural networks to learn anthropomorphic shape representations. Our first method attempts to complete the point cloud supplied by the input depth map by simply synthesizing the remaining information. We show that this method is capable of synthesizing the remainder of a point cloud with accuracies of 2.92±0.72 mm, and can be improved to accuracies of 2.55±0.75 mm when using an updated network architecture. Our second method fully synthesizes a complete point cloud foot scan from multiple virtual view points. We show that this method can produce foot scans with accuracies of 1.55±0.41 mm from a single input depth map. We performed additional experiments on real world foot scans captured using Kinect Fusion. We find that despite being trained only on a low resolution representation of foot shape, our models are able to recognize and synthesize reasonable complete point cloud scans. Our results suggest that our methods can be extended to work in the real world, with additional domain specific data.
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    Reconstruction of 3D Points From Uncalibrated Underwater Video
    (University of Waterloo, 2011-09-02T17:57:08Z) Cavan, Neil
    This thesis presents a 3D reconstruction software pipeline that is capable of generating point cloud data from uncalibrated underwater video. This research project was undertaken as a partnership with 2G Robotics, and the pipeline described in this thesis will become the 3D reconstruction engine for a software product that can generate photo-realistic 3D models from underwater video. The pipeline proceeds in three stages: video tracking, projective reconstruction, and autocalibration. Video tracking serves two functions: tracking recognizable feature points, as well as selecting well-spaced keyframes with a wide enough baseline to be used in the reconstruction. Video tracking is accomplished using Lucas-Kanade optical flow as implemented in the OpenCV toolkit. This simple and widely used method is well-suited to underwater video, which is taken by carefully piloted and slow-moving underwater vehicles. Projective reconstruction is the process of simultaneously calculating the motion of the cameras and the 3D location of observed points in the scene. This is accomplished using a geometric three-view technique. Results are presented showing that the projective reconstruction algorithm detailed here compares favourably to state-of-the-art methods. Autocalibration is the process of transforming a projective reconstruction, which is not suitable for visualization or measurement, into a metric space where it can be used. This is the most challenging part of the 3D reconstruction pipeline, and this thesis presents a novel autocalibration algorithm. Results are shown for two existing cost function-based methods in the literature which failed when applied to underwater video, as well as the proposed hybrid method. The hybrid method combines the best parts of its two parent methods, and produces good results on underwater video. Final results are shown for the 3D reconstruction pipeline operating on short under- water video sequences to produce visually accurate 3D point clouds of the scene, suitable for photorealistic rendering. Although further work remains to extend and improve the pipeline for operation on longer sequences, this thesis presents a proof-of-concept method for 3D reconstruction from uncalibrated underwater video.