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