A 3D stochastic cloud model for investigating the radiative properties of inhomogeneous cirrus clouds

(From author). Comments: First 3D stochastic/fractal model of cirrus; first detailed analysis & explanation of power spectra of ice water content, including first observations of 50-km scale break and mixing-induced steepening of spectrum; first demonstration of the potential effect of wind shear on radiative fluxes by changing fall-streak orientation. Has spawned work on the effect of 3D photon transport on the radiative effects of cirrus clouds.

People semantic description and re-identification from point cloud geometry

The automatic extraction of biometric descriptors of anonymous people is a challenging scenario in camera networks. This task is typically accomplished making use of visual information. Calibrated RGBD sensors make possible the extraction of point cloud information. We present a novel approach for people semantic description and re-identification using the individual point cloud information. The proposal combines the use of simple geometric features with point cloud features based on surface normals.

Visual Fixation for 3D Video Stabilization

Visual fixation is employed by humans and some animals to keep a specific 3D location at the center of the visual gaze. Inspired by this phenomenon in nature, this paper explores the idea to transfer this mechanism to the context of video stabilization for a handheld video camera. A novel approach is presented that stabilizes a video by fixating on automatically extracted 3D target points. This approach is different from existing automatic solutions that stabilize the video by smoothing. To determine the 3D target points, the recorded scene is analyzed with a stateof- the-art structure-from-motion algorithm, which estimates camera motion and reconstructs a 3D point cloud of the static scene objects. Special algorithms are presented that search either virtual or real 3D target points, which back-project close to the center of the image for as long a period of time as possible. The stabilization algorithm then transforms the original images of the sequence so that these 3D target points are kept exactly in the center of the image, which, in case of real 3D target points, produces a perfectly stable result at the image center. Furthermore, different methods of additional user interaction are investigated. It is shown that the stabilization process can easily be controlled and that it can be combined with state-of-theart tracking techniques in order to obtain a powerful image stabilization tool. The approach is evaluated on a variety of videos taken with a hand-held camera in natural scenes.

Methodology based on registration techniques for representing subjects and their deformations acquired from general purpose 3D sensors

In this thesis a methodology for representing 3D subjects and their deformations in adverse situations is studied. The study is focused in providing methods based on registration techniques to improve the data in situations where the sensor is working in the limit of its sensitivity. In order to do this, it is proposed two methods to overcome the problems which can difficult the process in these conditions. First a rigid registration based on model registration is presented, where the model of 3D planar markers is used. This model is estimated using a proposed method which improves its quality by taking into account prior knowledge of the marker. To study the deformations, it is proposed a framework to combine multiple spaces in a non-rigid registration technique. This proposal improves the quality of the alignment with a more robust matching process that makes use of all available input data. Moreover, this framework allows the registration of multiple spaces simultaneously providing a more general technique. Concretely, it is instantiated using colour and location in the matching process for 3D location registration.

Digital surface model, hillshade and orthophoto of the world's largest fossil oyster reef, links to GeoTIFFs

The world's largest fossil oyster reef, formed by the giant oyster Crassostrea gryphoides and located in Stetten (north of Vienna, Austria) is studied by Harzhauser et al., 2015, 2016; Djuricic et al., 2016. Digital documentation of the unique geological site is provided by terrestrial laser scanning (TLS) at the millimeter scale. Obtaining meaningful results is not merely a matter of data acquisition with a suitable device; it requires proper planning, data management, and postprocessing. Terrestrial laser scanning technology has a high potential for providing precise 3D mapping that serves as the basis for automatic object detection in different scenarios; however, it faces challenges in the presence of large amounts of data and the irregular geometry of an oyster reef. We provide a detailed description of the techniques and strategy used for data collection and processing in Djuricic et al., 2016. The use of laser scanning provided the ability to measure surface points of 46,840 (estimated) shells. They are up to 60-cm-long oyster specimens, and their surfaces are modeled with a high accuracy of 1 mm. In addition to laser scanning measurements, more than 300 photographs were captured, and an orthophoto mosaic was generated with a ground sampling distance (GSD) of 0.5 mm. This high-resolution 3D information and the photographic texture serve as the basis for ongoing and future geological and paleontological analyses. Moreover, they provide unprecedented documentation for conservation issues at a unique natural heritage site.

Utilizing Depth Information for Emerging 3D Applications

Thesis (Ph.D.)--University of Washington, 2016-06

Contextual classification of full waveform lidar data in the Wadden Sea

The classification of airborne lidar data is a relevant task in different disciplines. The information about the geometry and the full waveform can be used in order to classify the 3D point cloud. In Wadden Sea areas the classification of lidar data is of main interest for the scientific monitoring of coastal morphology and habitats, but it becomes a challenging task due to flat areas with hardly any discriminative objects. For the classification we combine a Conditional Random Fields framework with a Random Forests approach. By classifying in this way, we benefit from the consideration of context on the one hand and from the opportunity to utilise a high number of classification features on the other hand. We investigate the relevance of different features for the lidar points in coastal areas as well as for the interaction of neighbouring points.

Discontinuity Set Extractor

Disponible en Github: https://github.com/adririquelme/DSE

Improving the Accuracy of 3-D Reconstruction in Robotic Vision Applications

Thesis (Ph.D.)--University of Washington, 2016-08