Perceptually Validated Cross-Renderer Analytical BRDF Parameter Remapping
نویسندگان
چکیده
منابع مشابه
Perceptually Motivated BRDF Comparison using Single Image
Surface reflectance of real-world materials is now widely represented by the bidirectional reflectance distribution function (BRDF) and also by spatially varying representations such as SVBRDF and the bidirectional texture function (BTF). The raw surface reflectance measurements are typically compressed or fitted by analytical models, that always introduce a certain loss of accuracy. For its ev...
متن کاملPerceptually validated global/local deformations
Modal analysis techniques are often used to animate deformable objects in real time. In order to achieve performance improvements, the degrees of freedom of the problem may be reduced by using the main global deformations alone. However, this can lead to a reduction in quality and realism due to the lack of local behaviors. To solve this problem, we present a new method to add local deformation...
متن کاملToward a Perceptually Based Metric for BRDF Modeling
Measured materials are used in computer graphics to enhance the realism of synthetic images. They are often approximated with analytical models to improve storage efficiency and allow for importance sampling. However, the error metrics used in the optimization procedure do not have a perceptual basis and the obtained results do not always correspond to the best visual match. In this paper we pr...
متن کاملPerceptually relevant remapping of human somatotopy in 24 hours
Experience-dependent reorganisation of functional maps in the cerebral cortex is well described in the primary sensory cortices. However, there is relatively little evidence for such cortical reorganisation over the short-term. Using human somatosensory cortex as a model, we investigated the effects of a 24 hr gluing manipulation in which the right index and right middle fingers (digits 2 and 3...
متن کاملCross-Validated C4.5: Using Error Estimation for Automatic Parameter Selection
Machine learning algorithms for supervised learning are in wide use. An important issue in the use of these algorithms is how to set the parameters of the algorithm. While the default parameter values may be appropriate for a wide variety of tasks, they are not necessarily optimal for a given task. In this paper, we investigate the use of cross-validation to select parameters for the C4.5 decis...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Visualization and Computer Graphics
سال: 2020
ISSN: 1077-2626,1941-0506,2160-9306
DOI: 10.1109/tvcg.2018.2886877