Rendu volumique direct | EPFL Graph Search

Le rendu volumique direct est une technique utilisée pour afficher une projection 2D d'une série de données 3D. Rendu volumique direct Le rendu volumique direct nécessite que chaque valeur échantillonnée au sein du volume soit associée à une opacité et une couleur. Mathématiquement, cela revient à dire qu'on dispose d'une fonction de transfert f : \begin{array}[t]{rccl} f : & \mathcal{V} & \to & \mathcal{C} \times [0, 1] \ & \mathbf{x} & \mapsto & (c, \alpha) \end{array}

	où \mathcal{V} \sub \R^3 est la région de l'espace où la fonction est définie, et \mathcal{C} est l'espace de couleurs utilisé (par exemple {[0, 1]}^3 ou {[\![ 0, 255 ]\!]}^3si les couleurs sont définies par leurs valeurs RGB).

	La fonction de transfert est généralement modélisée par une fonction affine par morceaux. Cela revient à dire qu'elle est définie sur une partie finie de \mathcal{V} (par exemple une

Advanced Techniques in Optical Diffraction Tomography

Ahmed Bassam Sayed Ayoub Mohamed Emam

In this thesis, we study the 3 challenges described above. First, we study different reconstruction techniques and assess the fidelity of each reconstruction results by means of structured illumination and phase conjugation. By reconstructing the 3D refractive index of the sample using different algorithms (i.e. Born, Rytov, and Radon) and then perform a numerical back-propagation of experimentally measured structured illumination pattern we are able to assess the fidelity of each reconstruction algorithms without prior information about the 3D RI distribution of the sample.The second part of the thesis is concerned with the 3D reconstruction of samples using intensity-only measurements which the need to holographically acquire them. We show that using intensity-only measurements, we could still be able to reconstruct the 3D volume of the sample with edge-enhanced effects which was proven useful for drug delivery applications in which nano-particles were identified on the cell membrane of immune T-cells in a drug delivery studies. Such reconstruction technique would result in more robust imaging system where the commercial imaging microscope systems can be incorporated with LEDs for high-quality speckle noise-free imaging systems. In addition, we show that under certain conditions, we can be able to reconstruct the 3D refractive index distribution of different samples.The third part of the thesis is contributing to high-speed complex wave-front shaping using DMDs. In that part, new modulation technique is demonstrated that can boost the speed of the current time-multiplexing techniques by a factor of 32. The modulation technique is based on amplitude modulation where an amplitude modulator is synchronized withvthe DMD to modulate the intensity of each bit-plane of an 8-bit image and then all the modulated bit-planes are linearly added on the detector. Such modulation technique can be used not only for structured illumination microscopy but also for high-speed 3D printing applications as well as projectors.The last part is concerned with using deep learning approaches to solve the missing cone problem usually accompanied with optical imaging due to the limited numerical aperture of the imaging system. Two techniques are discussed; the first is based on using a physical model to enhance the quality of the 3D RI reconstruction and the second is based on using deep neural network to solve the missing cone problem.

EPFL2022

Radiative Backpropagation: An Adjoint Method for Lightning-Fast Differentiable Rendering

Wenzel Alban Jakob, Merlin Eléazar Nimier-David, Sébastien Nicolas Speierer

Physically based differentiable rendering has recently evolved into a powerful tool for solving inverse problems involving light. Methods in this area perform a differentiable simulation of the physical process of light transport and scattering to estimate partial derivatives relating scene parameters to pixels in the rendered image. Together with gradient-based optimization, such algorithms have interesting applications in diverse disciplines, e.g., to improve the reconstruction of 3D scenes, while accounting for interreflection and transparency, or to design meta-materials with specified optical properties. The most versatile differentiable rendering algorithms rely on reverse-mode differentiation to compute all requested derivatives at once, enabling optimization of scene descriptions with millions of free parameters. However, a severe limitation of the reverse-mode approach is that it requires a detailed transcript of the computation that is subsequently replayed to back-propagate derivatives to the scene parameters. The transcript of typical renderings is extremely large, exceeding the available system memory by many orders of magnitude, hence current methods are limited to simple scenes rendered at low resolutions and sample counts. We introduce radiative backpropagation, a fundamentally different approach to differentiable rendering that does not require a transcript, greatly improving its scalability and efficiency. Our main insight is that reverse-mode propagation through a rendering algorithm can be interpreted as the solution of a continuous transport problem involving the partial derivative of radiance with respect to the optimization objective. This quantity is "emitted" by sensors, "scattered" by the scene, and eventually "received" by objects with differentiable parameters. Differentiable rendering then decomposes into two separate primal and adjoint simulation steps that scale to complex scenes rendered at high resolutions. We also investigated biased variants of this algorithm and find that they considerably improve both runtime and convergence speed. We showcase an efficient GPU implementation of radiative backpropagation and compare its performance arid the quality of its gradients to prior work.

ASSOC COMPUTING MACHINERY2020

Automatic grouping of semantic keywords to improve image rendering

Mehmet Candemir, Albrecht Johannes Lindner, Sabine Süsstrunk

The ultimate goal of automatic image rendering is a system that gives at least as pleasing results as a human expert using an image manipulation program. In this article we demonstrate that the exploitation of semantic image keywords is a promising approach towards this ultimate goal. We develop a keyword clas- sification scheme specifically for the purpose of automatic image rendering. Further on, we propose a method to automatically classify keywords into these classes. We discuss the results based on experiments with a database of 40’000 images, annotated on average by five keywords each.

2010