Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging

A variety of techniques such as light field, structured illumination, and time-of-flight (TOF) are commonly used for depth acquisition in consumer imaging, robotics and many other applications. Unfortunately, each technique suffers from its individual limitations preventing robust depth sensing. In this paper, we explore the strengths and weaknesses of combining light field and time-of-flight imaging, particularly the feasibility of an on-chip implementation as a single hybrid depth sensor. We refer to this combination as depth field imaging. Depth fields combine light field advantages such as synthetic aperture refocusing with TOF imaging advantages such as high depth resolution and coded signal processing to resolve multipath interference. We show applications including synthesizing virtual apertures for TOF imaging, improved depth mapping through partial and scattering occluders, and single frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding, depth fields can improve depth sensing in the wild and generate new insights into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201

Coded exposure photography: motion deblurring using fluttered shutter

In a conventional single-exposure photograph, moving objects or moving cameras cause motion blur. The exposure time defines a temporal box filter that smears the moving object across the image by convolution. This box filter destroys important high-frequency spatial details so that deblurring via deconvolution becomes an illposed problem. Rather than leaving the shutter open for the entire exposure duration, we ”flutter ” the camera’s shutter open and closed during the chosen exposure time with a binary pseudo-random sequence. The flutter changes the box filter to a broad-band filter that preserves high-frequency spatial details in the blurred image and the corresponding deconvolution becomes a well-posed problem. We demonstrate that manually-specified point spread functions are sufficient for several challenging cases of motionblur removal including extremely large motions, textured backgrounds and partial occluders. ACM Transactions o Graphics (TOG

Three hypothesis algorithm with occlusion reasoning for multiple people tracking

This work proposes a detection-based tracking algorithm able to locate and keep the identity of multiple people, who may be occluded, in uncontrolled stationary environments. Our algorithm builds a tracking graph that models spatio-temporal relationships among attributes of interacting people to predict and resolve partial and total occlusions. When a total occlusion occurs, the algorithm generates various hypotheses about the location of the occluded person considering three cases: (a) the person keeps the same direction and speed, (b) the person follows the direction and speed of the occluder, and (c) the person remains motionless during occlusion. By analyzing the graph, our algorithm can detect trajectories produced by false alarms and estimate the location of missing or occluded people. Our algorithm performs acceptably under complex conditions, such as partial visibility of individuals getting inside or outside the scene, continuous interactions and occlusions among people, wrong or missing information on the detection of persons, as well as variation of the person’s appearance due to illumination changes and background-clutter distracters. Our algorithm was evaluated on test sequences in the field of intelligent surveillance achieving an overall precision of 93%. Results show that our tracking algorithm outperforms even trajectory-based state-of-the-art algorithms

Methods for Real-time Visualization and Interaction with Landforms

This thesis presents methods to enrich data modeling and analysis in the geoscience domain with a particular focus on geomorphological applications. First, a short overview of the relevant characteristics of the used remote sensing data and basics of its processing and visualization are provided. Then, two new methods for the visualization of vector-based maps on digital elevation models (DEMs) are presented. The first method uses a texture-based approach that generates a texture from the input maps at runtime taking into account the current viewpoint. In contrast to that, the second method utilizes the stencil buffer to create a mask in image space that is then used to render the map on top of the DEM. A particular challenge in this context is posed by the view-dependent level-of-detail representation of the terrain geometry. After suitable visualization methods for vector-based maps have been investigated, two landform mapping tools for the interactive generation of such maps are presented. The user can carry out the mapping directly on the textured digital elevation model and thus benefit from the 3D visualization of the relief. Additionally, semi-automatic image segmentation techniques are applied in order to reduce the amount of user interaction required and thus make the mapping process more efficient and convenient. The challenge in the adaption of the methods lies in the transfer of the algorithms to the quadtree representation of the data and in the application of out-of-core and hierarchical methods to ensure interactive performance. Although high-resolution remote sensing data are often available today, their effective resolution at steep slopes is rather low due to the oblique acquisition angle. For this reason, remote sensing data are suitable to only a limited extent for visualization as well as landform mapping purposes. To provide an easy way to supply additional imagery, an algorithm for registering uncalibrated photos to a textured digital elevation model is presented. A particular challenge in registering the images is posed by large variations in the photos concerning resolution, lighting conditions, seasonal changes, etc. The registered photos can be used to increase the visual quality of the textured DEM, in particular at steep slopes. To this end, a method is presented that combines several georegistered photos to textures for the DEM. The difficulty in this compositing process is to create a consistent appearance and avoid visible seams between the photos. In addition to that, the photos also provide valuable means to improve landform mapping. To this end, an extension of the landform mapping methods is presented that allows the utilization of the registered photos during mapping. This way, a detailed and exact mapping becomes feasible even at steep slopes

Visual attribute discovery and analyses from Web data

Visual attributes are important for describing and understanding an object’s appearance. For an object classification or recognition task, an algorithm needs to infer the visual attributes of an object to compare, categorize or recognize the objects. In a zero-shot learning scenario, the algorithm depends on the visual attributes to describe an unknown object since the training samples are not available. Because different object categories usually share some common attributes (e.g. many animals have four legs, a tail and fur), the act of explicitly modeling attributes not only allows previously learnt attributes to be transferred to a novel category but also reduces the number of training samples for the new category which can be important when the number of training samples is limited. Even though larger numbers of visual attributes help the algorithm to better describe an image, they also require a larger set of training data. In the supervised scenario, data collection can be both a costly and time-consuming process. To mitigate the data collection costs, this dissertation exploits the weakly-supervised data from the Web in order to construct computational methodologies for the discovery of visual attributes, as well as an analysis across time and domains. This dissertation first presents an automatic approach to learning hundreds of visual attributes from the open-world vocabulary on the Web using a convolutional neural network. The proposed method tries to understand visual attributes in terms of perception inside deep neural networks. By focusing on the analysis of neural activations, the system can identify the degree to which an attribute can be visually perceptible and can localize the visual attributes in an image. Moreover, the approach exploits the layered structure of the deep model to determine the semantic depth of the attributes. Beyond visual attribute discovery, this dissertation explores how visual styles (i.e., attributes that correspond to multiple visual concepts) change across time. These are referred to as visual trends. To this goal, this dissertation introduces several deep neural networks for estimating when objects were made together with the analyses of the neural activations and their degree of entropy to gain insights into the deep network. To utilize the dating of the historical object frameworks in real-world applications, the dating frameworks are applied to analyze the influence of vintage fashion on runway collections, as well as to analyze the influence of fashion on runway collections and on street fashion. Finally, this dissertation introduces an approach to recognizing and transferring visual attributes across domains in a realistic manner. Given two input images from two different domains: 1) a shopping image, and 2) a scene image, this dissertation proposes a generative adversarial network for transferring the product pixels from the shopping image to the scene image such that: 1) the output image looks realistic and 2) the visual attributes of the product are preserved. In summary, this dissertation utilizes the weakly-supervised data from the Web for the purposes of visual attribute discovery and an analysis across time and domains. Beyond the novel computational methodology for each problem, this dissertation demonstrates that the proposed approaches can be applied to many real-world applications such as dating historical objects, visual trend prediction and analysis, cross-domain image label transfer, cross-domain pixel transfer for home decoration, among others.Doctor of Philosoph

Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models

Revealing the Invisible: On the Extraction of Latent Information from Generalized Image Data

The desire to reveal the invisible in order to explain the world around us has been a source of impetus for technological and scientific progress throughout human history. Many of the phenomena that directly affect us cannot be sufficiently explained based on the observations using our primary senses alone. Often this is because their originating cause is either too small, too far away, or in other ways obstructed. To put it in other words: it is invisible to us. Without careful observation and experimentation, our models of the world remain inaccurate and research has to be conducted in order to improve our understanding of even the most basic effects. In this thesis, we1 are going to present our solutions to three challenging problems in visual computing, where a surprising amount of information is hidden in generalized image data and cannot easily be extracted by human observation or existing methods. We are able to extract the latent information using non-linear and discrete optimization methods based on physically motivated models and computer graphics methodology, such as ray tracing, real-time transient rendering, and image-based rendering

Dr.Bokeh: DiffeRentiable Occlusion-aware Bokeh Rendering

Bokeh is widely used in photography to draw attention to the subject while effectively isolating distractions in the background. Computational methods simulate bokeh effects without relying on a physical camera lens. However, in the realm of digital bokeh synthesis, the two main challenges for bokeh synthesis are color bleeding and partial occlusion at object boundaries. Our primary goal is to overcome these two major challenges using physics principles that define bokeh formation. To achieve this, we propose a novel and accurate filtering-based bokeh rendering equation and a physically-based occlusion-aware bokeh renderer, dubbed Dr.Bokeh, which addresses the aforementioned challenges during the rendering stage without the need of post-processing or data-driven approaches. Our rendering algorithm first preprocesses the input RGBD to obtain a layered scene representation. Dr.Bokeh then takes the layered representation and user-defined lens parameters to render photo-realistic lens blur. By softening non-differentiable operations, we make Dr.Bokeh differentiable such that it can be plugged into a machine-learning framework. We perform quantitative and qualitative evaluations on synthetic and real-world images to validate the effectiveness of the rendering quality and the differentiability of our method. We show Dr.Bokeh not only outperforms state-of-the-art bokeh rendering algorithms in terms of photo-realism but also improves the depth quality from depth-from-defocus