Photo-Realistic Scenes with Cast Shadows Show No Above/Below Search Asymmetries for Illumination Direction

Visual search is extended from the domain of polygonal figures presented on a uniform field to photo-realistic scenes containing target objects in dense, naturalistic backgrounds. The target in a trial is a computer-rendered rock protruding in depth from a "wall" of rocks of roughly similar size but different shapes. Subjects responded "present" when one rock appeared closer than the rest, owing to occlusions or cast shadows, and "absent" when all rocks appeared to be at the same depth. Results showed that cast shadows can significantly decrease reaction times compared to scenes with no cast shadows, in which the target was revealed only by occlusions of rocks behind it. A control experiment showed that cast shadows can be utilized even for displays involving rocks of several achromatic surface colors (dark through light), in which the shadow cast by the target rock was not the darkest region in the scene. Finally, in contrast with reports of experiments by others involving polygonal figures, we found no evidence for an effect of illumination direction (above vs. below) on search times.Office of Naval Research (N00014-94-1-0597, N00014-95-1-0409

Photo-Realistic Scenes with Cast Shadows Show No Above/Below Search Asymmetries for Illumination Direction

Visual search is extended from the domain of polygonal figures presented on a uniform field to photo-realistic scenes containing target objects in dense, naturalistic backgrounds. The target in a trial is a computer-rendered rock protruding in depth from a "wall" of rocks of roughly similar size but different shapes. Subjects responded "present" when one rock appeared closer than the rest, owing to occlusions or cast shadows, and "absent" when all rocks appeared to be at the same depth. Results showed that cast shadows can significantly decrease reaction times compared to scenes with no cast shadows, in which the target was revealed only by occlusions of rocks behind it. A control experiment showed that cast shadows can be utilized even for displays involving rocks of several achromatic surface colors (dark through light), in which the shadow cast by the target rock was not the darkest region in the scene. Finally, in contrast with reports of experiments by others involving polygonal figures, we found no evidence for an effect of illumination direction (above vs. below) on search times.Office of Naval Research (N00014-94-1-0597, N00014-95-1-0409

Exposure Render: An Interactive Photo-Realistic Volume Rendering Framework

The field of volume visualization has undergone rapid development during the past years, both due to advances in suitable computing hardware and due to the increasing availability of large volume datasets. Recent work has focused on increasing the visual realism in Direct Volume Rendering (DVR) by integrating a number of visually plausible but often effect-specific rendering techniques, for instance modeling of light occlusion and depth of field. Besides yielding more attractive renderings, especially the more realistic lighting has a positive effect on perceptual tasks. Although these new rendering techniques yield impressive results, they exhibit limitations in terms of their exibility and their performance. Monte Carlo ray tracing (MCRT), coupled with physically based light transport, is the de-facto standard for synthesizing highly realistic images in the graphics domain, although usually not from volumetric data. Due to the stochastic sampling of MCRT algorithms, numerous effects can be achieved in a relatively straight-forward fashion. For this reason, we have developed a practical framework that applies MCRT techniques also to direct volume rendering (DVR). With this work, we demonstrate that a host of realistic effects, including physically based lighting, can be simulated in a generic and flexible fashion, leading to interactive DVR with improved realism. In the hope that this improved approach to DVR will see more use in practice, we have made available our framework under a permissive open source license

Utilising path-vertex data to improve Monte Carlo global illumination.

Efficient techniques for photo-realistic rendering are in high demand across a wide array of industries. Notable applications include visual effects for film, entertainment and virtual reality. Less direct applications such as visualisation for architecture, lighting design and product development also rely on the synthesis of realistic and physically based illumination. Such applications assert ever increasing demands on light transport algorithms, requiring the computation of photo-realistic effects while handling complex geometry, light scattering models and illumination. Techniques based on Monte Carlo integration handle such scenarios elegantly and robustly, but despite seeing decades of focused research and wide commercial support, these methods and their derivatives still exhibit undesirable side effects that are yet to be resolved. In this thesis, Monte Carlo path tracing techniques are improved upon by utilizing path vertex data and intermediate radiance contributions readily available during rendering. This permits the development of novel progressive algorithms that render low noise global illumination while striving to maintain the desirable accuracy and convergence properties of unbiased methods. The thesis starts by presenting a discussion into optical phenomenon, physically based rendering and achieving photo realistic image synthesis. This is followed by in-depth discussion of the published theoretical and practical research in this field, with a focus on stochastic methods and modem rendering methodologies. This provides insight into the issues surrounding Monte Carlo integration both in the general and rendering specific contexts, along with an appreciation for the complexities of solving global light transport. Alternative methods that aim to address these issues are discussed, providing an insight into modem rendering paradigms and their characteristics. Thus, an understanding of the key aspects is obtained, that is necessary to build up and discuss the novel research and contributions to the field developed throughout this thesis. First, a path space filtering strategy is proposed that allows the path-based space of light transport to be classified into distinct subsets. This permits the novel combination of robust path tracing and recent progressive photon mapping algorithms to handle each subset based on the characteristics of the light transport in that space. This produces a hybrid progressive rendering technique that utilises the strengths of existing state of the art Monte Carlo and photon mapping methods to provide efficient and consistent rendering of complex scenes with vanishing bias. The second original contribution is a probabilistic image-based filtering and sample clustering framework that provides high quality previews of global illumination whilst remaining aware of high frequency detail and features in geometry, materials and the incident illumination. As will be seen, the challenges of edge-aware noise reduction are numerous and long standing, particularly when identifying high frequency features in noisy illumination signals. Discontinuities such as hard shadows and glossy reflections are commonly overlooked by progressive filtering techniques, however by dividing path space into multiple layers, once again based on utilising path vertex data, the overlapping illumination of varying intensities, colours and frequencies is more effectively handled. Thus noise is removed from each layer independent of features present in the remaining path space, effectively preserving such features

Free-viewpoint Indoor Neural Relighting from Multi-view Stereo

We introduce a neural relighting algorithm for captured indoors scenes, that allows interactive free-viewpoint navigation. Our method allows illumination to be changed synthetically, while coherently rendering cast shadows and complex glossy materials. We start with multiple images of the scene and a 3D mesh obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is well-explained as the sum of a view-independent diffuse component and a view-dependent glossy term concentrated around the mirror reflection direction. We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation. We generate these input maps by exploiting the best elements of both image-based and physically-based rendering. We sample the input views to estimate diffuse scene irradiance, and compute the new illumination caused by user-specified light sources using path tracing. To facilitate the network's understanding of materials and synthesize plausible glossy reflections, we reproject the views and compute mirror images. We train the network on a synthetic dataset where each scene is also reconstructed with MVS. We show results of our algorithm relighting real indoor scenes and performing free-viewpoint navigation with complex and realistic glossy reflections, which so far remained out of reach for view-synthesis techniques

Physical Adversarial Attack meets Computer Vision: A Decade Survey

Although Deep Neural Networks (DNNs) have achieved impressive results in computer vision, their exposed vulnerability to adversarial attacks remains a serious concern. A series of works has shown that by adding elaborate perturbations to images, DNNs could have catastrophic degradation in performance metrics. And this phenomenon does not only exist in the digital space but also in the physical space. Therefore, estimating the security of these DNNs-based systems is critical for safely deploying them in the real world, especially for security-critical applications, e.g., autonomous cars, video surveillance, and medical diagnosis. In this paper, we focus on physical adversarial attacks and provide a comprehensive survey of over 150 existing papers. We first clarify the concept of the physical adversarial attack and analyze its characteristics. Then, we define the adversarial medium, essential to perform attacks in the physical world. Next, we present the physical adversarial attack methods in task order: classification, detection, and re-identification, and introduce their performance in solving the trilemma: effectiveness, stealthiness, and robustness. In the end, we discuss the current challenges and potential future directions.Comment: 32 pages. Under Revie

Multi feature-rich synthetic colour to improve human visual perception of point clouds

Although point features have shown their usefulness in classification with Machine Learning, point cloud visualization enhancement methods focus mainly on lighting. The visualization of point features helps to improve the perception of the 3D environment. This paper proposes Multi Feature-Rich Synthetic Colour (MFRSC) as an alternative non-photorealistic colour approach of natural-coloured point clouds. The method is based on the selection of nine features (reflectance, return number, inclination, depth, height, point density, linearity, planarity, and scattering) associated with five human perception descriptors (edges, texture, shape, size, depth, orientation). The features are reduced to fit the RGB display channels. All feature permutations are analysed according to colour distance with the natural-coloured point cloud and Image Quality Assessment. As a result, the selected feature permutations allow a clear visualization of the scene's rendering objects, highlighting edges, planes, and volumetric objects. MFRSC effectively replaces natural colour, even with less distorted visualization according to BRISQUE, NIQUE and PIQE. In addition, the assignment of features in RGB channels enables the use of MFRSC in software that does not support colorization based on point attributes (most commercially available software). MFRSC can be combined with other non-photorealistic techniques such as Eye-Dome Lighting or Ambient Occlusion.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431F 2022/08Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Universidade de Vigo/CISU

AUTOMATIC IMAGE TO MODEL ALIGNMENT FOR PHOTO-REALISTIC URBAN MODEL RECONSTRUCTION

We introduce a hybrid approach in which images of an urban scene are automatically alignedwith a base geometry of the scene to determine model-relative external camera parameters. Thealgorithm takes as input a model of the scene and images with approximate external cameraparameters and aligns the images to the model by extracting the facades from the images andaligning the facades with the model by minimizing over a multivariate objective function. Theresulting image-pose pairs can be used to render photo-realistic views of the model via texturemapping.Several natural extensions to the base hybrid reconstruction technique are also introduced. Theseextensions, which include vanishing point based calibration refinement and video stream basedreconstruction, increase the accuracy of the base algorithm, reduce the amount of data that mustbe provided by the user as input to the algorithm, and provide a mechanism for automaticallycalibrating a large set of images for post processing steps such as automatic model enhancementand fly-through model visualization.Traditionally, photo-realistic urban reconstruction has been approached from purely image-basedor model-based approaches. Recently, research has been conducted on hybrid approaches, whichcombine the use of images and models. Such approaches typically require user assistance forcamera calibration. Our approach is an improvement over these methods because it does notrequire user assistance for camera calibration

Perceptually-Driven Decision Theory for Interactive Realistic Rendering

this paper we introduce a new approach to realistic rendering at interactive rates on commodity graphics hardware. The approach uses efficient perceptual metrics within a decision theoretic framework to optimally order rendering operations, producing images of the highest visual quality within system constraints. We demonstrate the usefulness of this approach for various applications such as diffuse texture caching, environment map prioritization and radiosity mesh simplification. Although here we address the problem of realistic rendering at interactive rates, the perceptually-based decision theoretic methodology we introduce can be usefully applied in many areas of computer graphic