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This paper presents a novel 3D recovery method based on structured light. This method unifies depth from focus (DFF) and depth from defocus (DFD) techniques with the use of a dynamic (de)focused projection. With this approach, the image acquisition system is specifically constructed to keep a whole object sharp in all of the captured images. Therefore, only the projected patterns experience different defocused deformations according to the object's depths. When the projected patterns are out of focus, their Point Spread Function (PSF) is assumed to follow a Gaussian distribution. The final depth is computed by the analysis of the relationship between the sets of PSFs obtained from different blurs and the variation of the object's depths. Our new depth estimation can be employed as a stand-alone strategy. It has no problem with occlusion and correspondence issues. Moreover, it handles textureless and partially reflective surfaces. The experimental results on real objects demonstrate the effective performance of our approach, providing reliable depth estimation and competitive time consumption. It uses fewer input images than DFF, and unlike DFD, it ensures that the PSF is locally unique.

Ce mémoire présente une nouvelle méthode pour l’acquisition 3D basée sur la lumière structurée. Cette méthode unifie les techniques de depth from focus (DFF) et depth from defocus (DFD) en utilisant une projection dynamique (dé)focalisée. Avec cette approche, le système d’acquisition d’images est construit de manière à conserver la totalité de l’objet nette sur toutes les images. Ainsi, seuls les motifs projetés sont soumis aux déformations de défocalisation en fonction de la profondeur de l’objet. Quand les motifs projetés ne sont pas focalisés, leurs Point Spread Function (PSF) sont assimilées à une distribution gaussienne. La profondeur finale est calculée en utilisant la relation entre les PSF de différents niveaux de flous et les variations de la profondeur de l’objet. Notre nouvelle estimation de la profondeur peut être utilisée indépendamment. Elle ne souffre pas de problèmes d’occultation ou de mise en correspondance. De plus, elle gère les surfaces sans texture et semi-réfléchissante. Les résultats expérimentaux sur des objets réels démontrent l’efficacité de notre approche, qui offre une estimation de la profondeur fiable et un temps de calcul réduit. La méthode utilise moins d’images que les approches DFF et contrairement aux approches DFD, elle assure que le PSF est localement unique / This paper presents a novel 3D recovery method based on structured light. This method unifies depth from focus (DFF) and depth from defocus (DFD) techniques with the use of a dynamic (de)focused projection. With this approach, the image acquisition system is specifically constructed to keep a whole object sharp in all of the captured images. Therefore, only the projected patterns experience different defocused deformations according to the object’s depths. When the projected patterns are out of focus, their Point Spread Function (PSF) is assumed to follow a Gaussian distribution. The final depth is computed by the analysis of the relationship between the sets of PSFs obtained from different blurs and the variation of the object’s depths. Our new depth estimation can be employed as a stand-alone strategy. It has no problem with occlusion and correspondence issues. Moreover, it handles textureless and partially reflective surfaces. The experimental results on real objects demonstrate the effective performance of our approach, providing reliable depth estimation and competitive time consumption. It uses fewer input images than DFF, and unlike DFD, it ensures that the PSF is locally unique.

<p dir="ltr">3D effect synthesis is crucial in image editing to enhance realism or visual appeal. Unlike classical graphics rendering, which relies on complete 3D geometries, 3D effect synthesis in im- age editing operates solely with 2D images as inputs. This shift presents significant challenges, primarily addressed by data-driven methods that learn to synthesize 3D effects in an end-to-end manner. However, these methods face limitations in the diversity of 3D effects they can produce and lack user control. For instance, existing shadow generation networks are restricted to produc- ing hard shadows without offering any user input for customization.</p><p dir="ltr">In this dissertation, we tackle the research question: <i>how can we synthesize controllable and realistic 3D effects in image editing when only 2D information is available? </i>Our investigation leads to four contributions. First, we introduce a neural network designed to create realistic soft shadows from an image cutout and a user-specified environmental light map. This approach is the first attempt in utilizing neural network for realistic soft shadow rendering in real-time. Second, we develop a novel 2.5D representation Pixel Height, tailored for the nuances of image editing. This representation not only forms the foundation of a new soft shadow rendering pipeline that provides intuitive user control, but also generalizes the soft shadow receivers to be general shadow receivers. Third, we present the mathematical relationship between the Pixel Height representation and 3D space. This connection facilitates the reconstruction of normals or depth from 2D scenes, broadening the scope for synthesizing comprehensive 3D lighting effects such as reflections and refractions. A 3D-aware buffer channels are also proposed to improve the synthesized soft shadow quality. Lastly, we introduce Dr.Bokeh, a differentiable bokeh renderer that extends traditional bokeh effect algorithms with better occlusion modeling to correct flaws existed in existing methods. With the more precise lens modeling, we show that Dr.Bokeh not only achieves the state-of-the-art bokeh rendering quality, but also pushes the boundary of depth-from-defocus problem.</p><p dir="ltr">Our work in controllable 3D effect synthesis represents a pioneering effort in image editing, laying the groundwork for future lighting effect synthesis in various image editing applications. Moreover, the improvements to filtering-based bokeh rendering could significantly enhance com- mercial products, such as the portrait mode feature on smartphones.</p>

This thesis deals with optical distance measurement and 3D scene measurement using focusing techniques with focus on confocal microscopy, depth from focus and depth from defocus. Theoretical part of the thesis is about different approaches to depth map generation and also about micro image defocusing technique for measuring refractive index of transparent materials. Then the camera calibration for focused techniques is described. In the next part of the thesis is described experimentally verification of depth from focus and depth from defocus techniques. For the first technique are shown results of depth map generation and for the second technique is shown comparison between measured distance values and real distance values. Finally, the discussed techniques are compared and evaluated.