Image-based 3D reconstruction has been employed in industrial metrology for micro measurements and quality control purposes. However, generating a highly-detailed and reliable 3D reconstruction of non-collaborative surfaces (textureless, shiny, and transparent) is still an open issue. This thesis presents various methodologies to successfully generate a highly-detailed and reliable 3D reconstruction of non-collaborative objects using the proposed photometric stereo image acquisition system. The first proposed method employs geometric construction to integrate photogrammetry and photometric stereo in order to overcome each technique's limitations and to leverage each technique's strengths in order to reconstruct an accurate and high-resolution topography of non-collaborative surfaces. This method uses accurate photogrammetric 3D measurements to rectify the global shape deviation of photometric stereo meanwhile uses photometric stereo to recover the high detailed topography of the object. The second method combines the high spatial frequencies of photometric stereo depth map with the low frequencies of photogrammetric depth map in frequency domain to produce accurate low frequencies while retaining high frequencies. For the third approach, we utilize light directionality to improve texture quality by leveraging shade and shadow phenomena using the proposed image-capturing system that employs several light sources for highlighting roughness and microstructures on the surface. And finally, we present two methods that effectively orient images by leveraging the low-contrast textures highlighted on object surfaces (roughness and 3D microstructures) using proper lighting system. Various objects with different surface characteristics including textureless, reflective, and transparent are used to evaluate different proposed approaches. To assess the accuracy of each approach, a comprehensive comparison between reference data and generated 3D points is provided.

This thesis explores the use of computer vision to facilitate three different processes of a quarry's operation. The first is the blasting process. This is where operators determine where to drill in order to execute an efficient and safe blast. Having an operator manually determine the drilling angles and positions can lead to inefficient and dangerous blasts. By using two cameras, oriented vertically, and separated by a fixed baseline, Structure from Motion techniques can be used to create a scaled 3D model of a bench. This can then be analyzed to provide operators with borehole locations and drilling angles in relation to fixed reference targets. The second process explored is the crushing process, where the rocks pass through different crushers that reduce the rocks into smaller sizes. The crushed rocks are then dropped onto a moving conveyor belt. The maximum dimension of the rocks exiting the crushers should not exceed size thresholds that are specific to each crusher. This thesis presents a 2D vision system capable of estimating the size distribution of the rocks by attempting to segment the rocks in each image. The size distribution, based on the maximum dimension of each rock, is estimated by finding the maximum dimension in the image in pixels and converting that to inches. The third process of the quarry operations explored is where the final product is piled up to form stockpiles. For inventory purposes, operators often carry out a manual estimation of the size of a the stockpile. This thesis presents a vision system capable of providing a more accurate estimate for the size of the stockpile by using Structure from Motion techniques to create a 3D reconstruction. User interaction helps to find the points that are relevant to the stockpile in the resulting point cloud, which are then used to estimate the volume. / Master of Science

Sample collection is a common task for mobile robots and there are a variety of manipulators available to perform this operation. This thesis presents a novel scoop sample collection system design which is able to both collect and contain a sample using the same hardware. To ease the operator burden during sampling the scoop system is paired with new semi-autonomous and fully autonomous collection techniques. These are derived from data provided by colored 3D point clouds produced via image-based 3D reconstructions. A custom robotic mobility platform, the Scoopbot, is introduced to perform completely automated imaging of the sampling area and also to pick up the desired sample. The Scoopbot is wirelessly controlled by a base station computer which runs software to create and analyze the 3D point cloud models. Relevant sample parameters, such as dimensions and volume, are calculated from the reconstruction and reported to the operator. During tests of the system in full (48 images) and fast (6-8 images) modes the Scoopbot was able to identify and retrieve a sample without any human intervention. Finally, a new building crack detection algorithm (CDA) is created to use the 3D point cloud outputs from image sets gathered by a mobile robot. The CDA was shown to successfully identify and color-code several cracks in a full-scale concrete building element. / Master of Science

Image-based 3D reconstruction refers to the capture and virtual reconstruction of real scenes, through the use of ordinary camera sensors. A common approach is the use of the algorithms Structure from Motion, Multi-view Stereo and Poisson Surface Reconstruction, that fares well for many types of scenes. However, a problem that this pipeline suffers from is that it often falters when it comes to texture-less surfaces and areas, such as those found in man-made environments. Building facades, roads and walls often lack detail and easily trackable feature points, making this approach less than ideal for such scenes. To remedy this weakness, this thesis investigates an expanded approach, incorporating line segment detection and line cloud generation into the already existing point cloud-based pipeline. Texture-less objects such as building facades, windows and roofs are well-suited for line segment detection, and line clouds are fitting for encoding 3D positional data in scenes consisting mostly of objects featuring many straight lines. A number of approaches have been explored in order to determine the usefulness of line clouds in this context, each of them addressing different aspects of the reconstruction procedure.