Perceptually Optimized Super Resolution

Modern deep-learning based super-resolution techniques process images and videos independently of the underlying content and viewing conditions. However, the sensitivity of the human visual system to image details changes depending on the underlying content characteristics, such as spatial frequency, luminance, color, contrast, or motion. This observation hints that computational resources spent on up-sampling visual content may be wasted whenever a viewer cannot resolve the results. Motivated by this observation, we propose a perceptually inspired and architecture-agnostic approach for controlling the visual quality and efficiency of super-resolution techniques. The core is a perceptual model that dynamically guides super-resolution methods according to the human's sensitivity to image details. Our technique leverages the limitations of the human visual system to improve the efficiency of super-resolution techniques by focusing computational resources on perceptually important regions; judged on the basis of factors such as adapting luminance, contrast, spatial frequency, motion, and viewing conditions. We demonstrate the application of our proposed model in combination with network branching, and network complexity reduction to improve the computational efficiency of super-resolution methods without visible quality loss. Quantitative and qualitative evaluations, including user studies, demonstrate the effectiveness of our approach in reducing FLOPS by factors of 2$\mathbf{x}$ and greater, without sacrificing perceived quality.

Puzzle Similarity: A Perceptually-guided No-Reference Metric for Artifact Detection in 3D Scene Reconstructions

Modern reconstruction techniques can effectively model complex 3D scenes from sparse 2D views. However, automatically assessing the quality of novel views and identifying artifacts is challenging due to the lack of ground truth images and the limitations of no-reference image metrics in predicting detailed artifact maps. The absence of such quality metrics hinders accurate predictions of the quality of generated views and limits the adoption of post-processing techniques, such as inpainting, to enhance reconstruction quality. In this work, we propose a new no-reference metric, Puzzle Similarity, which is designed to localize artifacts in novel views. Our approach utilizes image patch statistics from the input views to establish a scene-specific distribution that is later used to identify poorly reconstructed regions in the novel views. We test and evaluate our method in the context of 3D reconstruction; to this end, we collected a novel dataset of human quality assessment in unseen reconstructed views. Through this dataset, we demonstrate that our method can not only successfully localize artifacts in novel views, correlating with human assessment, but do so without direct references. Surprisingly, our metric outperforms both no-reference metrics and popular full-reference image metrics. We can leverage our new metric to enhance applications like automatic image restoration, guided acquisition, or 3D reconstruction from sparse inputs.

Volumetric Primitives for Modeling and Rendering Scattering and Emissive Media

We propose a volumetric representation based on primitives to model scattering and emissive media. Accurate scene representations enabling efficient rendering are essential for many computer graphics applications. General and unified representations that can handle surface and volume-based representations simultaneously, allowing for physically accurate modeling, remain a research challenge. Inspired by recent methods for scene reconstruction that leverage mixtures of 3D Gaussians to model radiance fields, we formalize and generalize the modeling of scattering and emissive media using mixtures of simple kernel-based volumetric primitives. We introduce closed-form solutions for transmittance and free-flight distance sampling for 3D Gaussian kernels, and propose several optimizations to use our method efficiently within any off-the-shelf volumetric path tracer by leveraging ray tracing for efficiently querying the medium. We demonstrate our method as an alternative to other forms of volume modeling (e.g. voxel grid-based representations) for forward and inverse rendering of scattering media. Furthermore, we adapt our method to the problem of radiance field optimization and rendering, and demonstrate comparable performance to the state of the art, while providing additional flexibility in terms of performance and usability.

A Learned Radiance-Field Representation for Complex Luminaires

We propose an efficient method for rendering complex luminaires using a high-quality octree-based representation of the luminaire emission. Complex luminaires are a particularly challenging problem in rendering, due to their caustic light paths inside the luminaire. We reduce the geometric complexity of luminaires by using a simple proxy geometry and encode the visually-complex emitted light field by using a neural radiance field. We tackle the multiple challenges of using NeRFs for representing luminaires, including their high dynamic range, high-frequency content and null-emission areas, by proposing a specialized loss function. For rendering, we distill our luminaires' NeRF into a Plenoctree, which we can be easily integrated into traditional rendering systems. Our approach allows for speed-ups of up to 2 orders of magnitude in scenes containing complex luminaires introducing minimal error.