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We present an improved uniform subdivision based discrete and continuous collision detection approach for deformable objects consisting of triangle meshes without any assumption about triangle size. A previously proposed technique using control bits can effectively eliminate redundant object pairs appearing in multiple cells, but this scheme requires the grid cell size adapted to the largest object, and efficiency tends to be severely impaired when object size varies strongly. In this paper, we discuss an approach that virtually subdivides large triangles into a number of child triangles to enable the use of a smaller, better suited cell size, resulting in a considerable decrease in the number of collision tests in the broad phase, with a corresponding reduced memory requirement. The virtual subdivision is used only for the purpose of collision detection and is recomputed each frame, with the original mesh retained for collision response and physical simulation. Our method exploits the benefits of GPU architecture to accelerate the computationally intensive task for improved performance. The results show that the method provides speedups by comparing performance with existing methods.

We propose an end-to-end dehazing model based on deep learning (CNN network) and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing. Compare to the previously proposed dehazing network, the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection, and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions. A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in terms of PSNR, SSIM, and subjective visual quality. In addition, it achieved a good performance in speed by using EfficientNet B0 as a feature extractor. We find that only using high-level semantic features can not effectively obtain all the information in the image. The FPN structure used in this paper can effectively integrate the high-level semantics and the low-level semantics, and can better take into account the global and local features. The five feature maps with different sizes are not simply weighted and fused. In order to keep all their information, we put them all together and get the final features through decode layers. At the same time, we have done a comparative experiment between ResNet with FPN and EfficientNet with BiFPN. It is proved that EfficientNet with BiFPN can obtain image features more efficiently. Therefore, EfficientNet with BiFPN is chosen as our network feature extraction.

Real cloth exhibits bending effects, such as residual curvatures and permanent wrinkles. These are typically explained by bending plastic deformation due to internal friction in the fibre and yarn structure. Internal friction also gives rise to energy dissipation which significantly affects cloth dynamic behaviour. In textile research, hysteresis is used to analyse these effects, and can be modelled using complex friction terms at the fabric geometric structure level. The hysteresis loop is central to the modelling and understanding of elastic and inelastic (plastic) behaviour, and is often measured as a physical characteristic to analyse and predict fabric behaviour. However, in cloth simulation in computer graphics the use of hysteresis to capture these effects has not been reported so far. Existing approaches have typically used plasticity models for simulating plastic deformation. In this paper, we report on our investigation into experiments using a simple mathematical approximation to an ideal hysteresis loop at a high level to capture the previously mentioned effects. Fatigue weakening effects during repeated flexural deformation are also considered based on the hysteresis model. Comparisons with previous bending models and plasticity methods are provided to point out differences and advantages. The method requires only incremental extra computation time.