In this study, we present a new and innovative framework for acquiring high-quality SVBRDF maps. Our approach addresses the limi-tations of the current methods and proposes a new solution. The core of our method is a simple hardware setup consisting of a consumer-level camera, LED lights, and a carefully designed network that can accurately obtain the high-quality SVBRDF properties of a nearly planar object. By capturing a flexible number of images of an object, our network uses different subnetworks to train different property maps and employs appropriate loss functions for each of them. To further enhance the quality of the maps, we improved the network structure by adding a novel skip connection that connects the encoder and decoder withglobal features. Through extensive experimentation using both synthetic and real-world materials, our results demonstrate that our method outperforms previous methods and produces superior results. Furthermore, our proposed setup can also be used to acquire physically based rendering maps of special materials.

Color pencil drawing is well-loved due to its rich expressiveness. This paper proposes an approach for generating feature-preserving color pencil drawings from photographs. To mimic the tonal style of color pencil drawings, which are much lighter and have relatively lower saturation than photographs, we devise a lightness enhancement mapping and a saturation reduction mapping. The lightness mapping is a monotonically decreasing derivative function, which not only increases lightness but also preserves input photograph features. Color saturation is usually related to lightness, so we suppress the saturation dependent on lightness to yield a harmonious tone. Finally, two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are consistent. Comprehensive experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

In digital furniture design, skillful designers usually use professional software to create new furniture designs with various textures and then take advantage of rendering tools to produce eye-catching design results. Generally, a fine-grained furniture model holds many geometric details, inducing significant time cost to model rendering and large data size for storage that are not desired in application scenarios where efficiency is greatly emphasized. To accelerate the rendering process while keeping good rendering results as many as possible, we develop a novel decimation technique which not only reduces the number of faces on furniture models, but also retains their geometric and texture features. Two metrics are utilized in our approach to measure the distortion of texture features. Considering these two metrics as guidance for decimation, high texture distortion can be avoided in simplifying the geometric models. Therefore, we are able to build multi-level representations with different detail levels based on the initial design. Our experimental results show that the developed technique can achieve excellent visual effects on the decimated furniture model.