We describe and experimentally validate an end-to-end simulation of a digital camera. The simulation models the spectral radiance of 3D-scenes, formation of the spectral irradiance by multi-element optics, and conversion of the irradiance to digital values by the image sensor. We quantify the accuracy of the simulation by comparing real and simulated images of a precisely constructed, three-dimensional high dynamic range test scene. Validated end-to-end software simulation of a digital camera can accelerate innovation by reducing many of the time-consuming and expensive steps in designing, building and evaluating image systems.
Bidirectional Texture Function (BTF) is one of the methods to reproduce realistic images in Computer Graphics (CG). This is a technique that can be applied to texture mapping with changing lighting and viewing directions and can reproduce realistic appearance by a simple and high-speed processing. However, in the BTF method, a large amount of texture data is generally measured and stored in advance. In this paper, in order to address the problems related to the measurement time and the texture data size in the BTF reproduction, we a method to generate a BTF image dataset using deep learning. We recovery texture images under various azimuth lighting conditions from a single texture image. For achieving this goal, we applied the U-Net to our BTF recovery. The restored and original texture images are compared using SSIM. It will be confirmed that the reproducibility of fabric and wood textures is high.
We describe an open-source simulator that creates sensor irradiance and sensor images of typical automotive scenes in urban settings. The purpose of the system is to support camera design and testing for automotive applications. The user can specify scene parameters (e.g., scene type, road type, traffic density, time of day) to assemble a large number of random scenes from graphics assets stored in a database. The sensor irradiance is generated using quantitative computer graphics methods, and the sensor images are created using image systems sensor simulation. The synthetic sensor images have pixel level annotations; hence, they can be used to train and evaluate neural networks for imaging tasks, such as object detection and classification. The end-to-end simulation system supports quantitative assessment ? from scene to camera to network accuracy for automotive applications.
In this paper, we construct a model for cross-modal perception of glossiness by investigating the interaction between sounds and graphics. First, we conduct evaluation experiments on cross-modal glossiness perception using sounds and graphics stimuli. There are three types of stimuli in the experiments. The stimuli are visual stimuli (22 stimuli), audio stimuli (15 stimuli) and audiovisual stimuli (330 stimuli). Also, there are three sections in the experiments. The first one is a visual experiment, the second one is an audiovisual experiment, and the third one is an auditory experiment. For the evaluation of glossiness, the magnitude evaluation method is applied. Second, we analyze the influence of sounds on glossiness perception from the experimental results. The results suggest that the cross-modal perception of glossiness can be represented as a combination of visual-only perception and auditory-only perception. Then, based on the results, we construct a model by a linear sum of computer graphics and sound parameters. Finally, we confirm the feasibility of the cross-modal glossiness perception model through a validation experiment.