Dynamic BFECC Characteristic Mapping method for fluid simulations

In this paper, we present a new numerical method for advection in fluid simulation. The method is built on the Characteristic Mapping method. Advection is solved via grid mapping function. The mapping function is maintained with higher order accuracy BFECC method and dynamically reset to identity mapping whenever an error criterion is met. Dealing with mapping function in such a way results in a more accurate mapping function and more details can be captured easily with this mapping function. Our error criterion also allows one to control the level of details of fluid simulation by simply adjusting one parameter. Details of implementation of our method are discussed and we present several techniques for improving its efficiency. Both quantitative and visual experiments were performed to test our method. The results show that our method brings significant improvement in accuracy and is efficient in capturing fluid details. © 2014 Springer-Verlag Berlin Heidelberg.In this paper, we present a new numerical method for advection in fluid simulation. The method is built on the Characteristic Mapping method. Advection is solved via grid mapping function. The mapping function is maintained with higher order accuracy BFECC method and dynamically reset to identity mapping whenever an error criterion is met. Dealing with mapping function in such a way results in a more accurate mapping function and more details can be captured easily with this mapping function. Our error criterion also allows one to control the level of details of fluid simulation by simply adjusting one parameter. Details of implementation of our method are discussed and we present several techniques for improving its efficiency. Both quantitative and visual experiments were performed to test our method. The results show that our method brings significant improvement in accuracy and is efficient in capturing fluid details. © 2014 Springer-Verlag Berlin Heidelberg

Multi-resolution shadow mapping using CUDA rasterizer

Shadow mapping is a fast and easy to use method to produce hard shadows. However, it introduces aliasing due to its uniform sampling strategy and limited shadow map resolution. In this paper, we propose a memory efficient algorithm to render high quality shadows. Our algorithm is based on a multi-resolution shadow map structure, which includes a conventional shadow map for scene regions where a low-resolution shadow map is sufficient, and a high-resolution patch buffer to capture scene regions that are susceptible to aliasing. With this data structure, we are able to capture shadow details with far less memory footprint than conventional shadow mapping. In order to maintain an appropriate performance compared to conventional shadow mapping, we designed a customized CUDA rasterizer to render the high-resolution patches. © 2013 IEEE.Shadow mapping is a fast and easy to use method to produce hard shadows. However, it introduces aliasing due to its uniform sampling strategy and limited shadow map resolution. In this paper, we propose a memory efficient algorithm to render high quality shadows. Our algorithm is based on a multi-resolution shadow map structure, which includes a conventional shadow map for scene regions where a low-resolution shadow map is sufficient, and a high-resolution patch buffer to capture scene regions that are susceptible to aliasing. With this data structure, we are able to capture shadow details with far less memory footprint than conventional shadow mapping. In order to maintain an appropriate performance compared to conventional shadow mapping, we designed a customized CUDA rasterizer to render the high-resolution patches. © 2013 IEEE

Accurate and efficient cross-domain visual matching leveraging multiple feature representations

Cross-domain visual matching aims at finding visually similar images across a wide range of visual domains, and has shown a practical impact on a number of applications. Unfortunately, the state-of-the-art approach, which estimates the relative importance of the single feature dimensions still suffers from low matching accuracy and high time cost. To this end, this paper proposes a novel cross-domain visual matching framework leveraging multiple feature representations. To integrate the discriminative power of multiple features, we develop a data-driven, query specific feature fusion model, which estimates the relative importance of the individual feature dimensions as well as the weight vector among multiple features simultaneously. Moreover, to alleviate the computational burden of an exhaustive subimage search, we design a speedup scheme, which employs hyperplane hashing for rapidly collecting the hard-negatives. Extensive experiments carried out on various matching tasks demonstrate that the proposed approach outperforms the state-of-the-art in both accuracy and efficiency. © 2013 Springer-Verlag Berlin Heidelberg.Cross-domain visual matching aims at finding visually similar images across a wide range of visual domains, and has shown a practical impact on a number of applications. Unfortunately, the state-of-the-art approach, which estimates the relative importance of the single feature dimensions still suffers from low matching accuracy and high time cost. To this end, this paper proposes a novel cross-domain visual matching framework leveraging multiple feature representations. To integrate the discriminative power of multiple features, we develop a data-driven, query specific feature fusion model, which estimates the relative importance of the individual feature dimensions as well as the weight vector among multiple features simultaneously. Moreover, to alleviate the computational burden of an exhaustive subimage search, we design a speedup scheme, which employs hyperplane hashing for rapidly collecting the hard-negatives. Extensive experiments carried out on various matching tasks demonstrate that the proposed approach outperforms the state-of-the-art in both accuracy and efficiency. © 2013 Springer-Verlag Berlin Heidelberg

Multi-layer screen-space ambient occlusion using hybrid sampling

Ambient occlusion is a shading method which approximates the global illumination by taking into account attenuation of light due to occlusion[Christensen 2002; Sergey et al. 1998; Andrei et al. 2003]. Screen-space ambient occlusion is a scene-independent improvement which performs the visibility test using depth buffer on GPU in real time. Shanmugam et al. [Shanmugam et al. 2007] proposes a real-time multi-pass hardware assisted ambient occlusion method. The algorithm randomly samples the normal/depth buffer and reconstructs the surface using spheres that can be projected to screen, but has the overocclusion issue. Bavoil et al. [Bavoil et al. 2008] approximates the ambient occlusion illumination using a image-space horizon-based method. However, the scenes will also be overoccluded due to the inconsideration of the layers behind the frontmost one. Moreover, the rough approximation of the geometry occluder makes the shadows lack of rich details. In this paper we propose a new solution to approximate ambient occlusion using a hybrid sampling method of both above methods. The surfaces can be more accurately depicted using spheres and the geometry occluder under the horizon plane can be discarded to address the overocclusion issue. In addition, a multi-layer method is utilized to future alleviate overocclusion using the depth information of all the layers. Experiments show that our algorithm can generate more realistic results for complex scenes with rich details, with comparable performance. © 2013 ACM.Ambient occlusion is a shading method which approximates the global illumination by taking into account attenuation of light due to occlusion[Christensen 2002; Sergey et al. 1998; Andrei et al. 2003]. Screen-space ambient occlusion is a scene-independent improvement which performs the visibility test using depth buffer on GPU in real time. Shanmugam et al. [Shanmugam et al. 2007] proposes a real-time multi-pass hardware assisted ambient occlusion method. The algorithm randomly samples the normal/depth buffer and reconstructs the surface using spheres that can be projected to screen, but has the overocclusion issue. Bavoil et al. [Bavoil et al. 2008] approximates the ambient occlusion illumination using a image-space horizon-based method. However, the scenes will also be overoccluded due to the inconsideration of the layers behind the frontmost one. Moreover, the rough approximation of the geometry occluder makes the shadows lack of rich details. In this paper we propose a new solution to approximate ambient occlusion using a hybrid sampling method of both above methods. The surfaces can be more accurately depicted using spheres and the geometry occluder under the horizon plane can be discarded to address the overocclusion issue. In addition, a multi-layer method is utilized to future alleviate overocclusion using the depth information of all the layers. Experiments show that our algorithm can generate more realistic results for complex scenes with rich details, with comparable performance. © 2013 ACM