Plenoptic Signal Processing for Robust Vision in Field Robotics

This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications

Plenoptic Signal Processing for Robust Vision in Field Robotics

This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications

Efficient acquisition, representation and rendering of light fields

In this thesis we discuss the representation of three-dimensional scenes using image data (image-based rendering), and more precisely the so-called light field approach. We start with an up-to-date survey on previous work in this young field of research. Then we propose a light field representation based on image data and additional per-pixel depth values. This enables us to reconstruct arbitrary views of the scene in an efficient way and with high quality. Furtermore, we can use the same representation to determine optimal reference views during the acquisition of a light field. We further present the so-called free form parameterization, which allows for a relatively free placement of reference views. Finally, we demonstrate a prototype of the Lumi-Shelf system, which acquires, transmits, and renders the light field of a dynamic scene at multiple frames per second.Diese Doktorarbeit beschäftigt sich mit der Repräsentierung dreidimensionaler Szenen durch Bilddaten (engl. image-based rendering, deutsch bildbasierte Bildsynthese), speziell mit dem Ansatz des sog. Lichtfelds. Nach einem aktuellen Überblick über bisherige Arbeiten in diesem jungen Forschungsgebiet stellen wir eine Datenrepräsentation vor, die auf Bilddaten mit zusätzlichen Tiefenwerten basiert. Damit sind wir in der Lage, beliebige Ansichten der Szene effizient und in hoher Qualität zu rekonstruieren sowie die optimalen Referenz-Ansichten bei der Akquisition eines Lichtfelds zu bestimmen. Weiterhin präsentieren wir die sog. Freiform-Parametrisierung, die eine relativ freie Anordnung der Referenz-Ansichten erlaubt. Abschließend demonstrieren wir einen Prototyp des Lumishelf-Systems, welches die Aufnahme, Übertragung und Darstellung des Lichtfeldes einer dynamischen Szene mit mehreren Bildern pro Sekunde ermöglicht

Efficient acquisition, representation and rendering of light fields

In this thesis we discuss the representation of three-dimensional scenes using image data (image-based rendering), and more precisely the so-called light field approach. We start with an up-to-date survey on previous work in this young field of research. Then we propose a light field representation based on image data and additional per-pixel depth values. This enables us to reconstruct arbitrary views of the scene in an efficient way and with high quality. Furtermore, we can use the same representation to determine optimal reference views during the acquisition of a light field. We further present the so-called free form parameterization, which allows for a relatively free placement of reference views. Finally, we demonstrate a prototype of the Lumi-Shelf system, which acquires, transmits, and renders the light field of a dynamic scene at multiple frames per second.Diese Doktorarbeit beschäftigt sich mit der Repräsentierung dreidimensionaler Szenen durch Bilddaten (engl. image-based rendering, deutsch bildbasierte Bildsynthese), speziell mit dem Ansatz des sog. Lichtfelds. Nach einem aktuellen Überblick über bisherige Arbeiten in diesem jungen Forschungsgebiet stellen wir eine Datenrepräsentation vor, die auf Bilddaten mit zusätzlichen Tiefenwerten basiert. Damit sind wir in der Lage, beliebige Ansichten der Szene effizient und in hoher Qualität zu rekonstruieren sowie die optimalen Referenz-Ansichten bei der Akquisition eines Lichtfelds zu bestimmen. Weiterhin präsentieren wir die sog. Freiform-Parametrisierung, die eine relativ freie Anordnung der Referenz-Ansichten erlaubt. Abschließend demonstrieren wir einen Prototyp des Lumishelf-Systems, welches die Aufnahme, Übertragung und Darstellung des Lichtfeldes einer dynamischen Szene mit mehreren Bildern pro Sekunde ermöglicht

Automatic image-based scene model acquisition and visualization

In this paper we present the design and realization of an integrated automatic framework for capturing, reconstructing and rendering real world scenes with image-based techniques. We use uncalibrated 2D video streams from a consumer video camera recording the scene data in an overlapping move pattern and reconstruct a calibrated view sequence from it. This view data is then compressed and transformed to a lightfield representation used for storage and rendering. The system design allows the usage of different compression techniques and lightfield parameterizations. We show the different steps of the process chain and describe the details of the underlying system architecture and its modular design which is capable of transparently supporting various different file formats and data representations required at different stages. As a result we present the application of the framework with a sample scene. Finally we outline current limitations and possibilities for future extensions of the system.