3D model retrieval virtual reality applications. In can benefit many downstream this paper, we propose a new sketch-based 3D model retrieval framework by coupling local features and manifold ranking. At technical fron...3D model retrieval virtual reality applications. In can benefit many downstream this paper, we propose a new sketch-based 3D model retrieval framework by coupling local features and manifold ranking. At technical fronts, we exploit spatial pyramids based local structures to facilitate the efficient construction of feature descriptors. Meanwhile, we propose an improved manifold ranking method, wherein all the categories between arbitrary model pairs will be taken into account. Since the smooth and detail-preserving line drawings of 3D model are important for sketch-based 3D model retrieval, the Difference of Gaussians （DOG） method is employed to extract the line drawings over the projected depth images of 3D model, and Bezier Curve is then adopted to further optimize the extracted line drawing. On that basis, we develop a 3D model retrieval engine to verify our method. We have conducted extensive experiments over various public benchmarks, and have made comprehensive comparisons with some state-of-the-art 3D retrieval methods. All the evaluation results based on the widely-used indicators prove the superiority of our method in accuracy, reliability, robustness, and versatility.展开更多
The 3D object tracking from a monocular RGB image is a challenging task.Although popular color and edgebased methods have been well studied,they are only applicable to certain cases and new solutions to the challenges...The 3D object tracking from a monocular RGB image is a challenging task.Although popular color and edgebased methods have been well studied,they are only applicable to certain cases and new solutions to the challenges in real environment must be developed.In this paper,we propose a robust 3D object tracking method with adaptively weighted local bundles called AWLB tracker to handle more complicated cases.Each bundle represents a local region containing a set of local features.To alleviate the negative effect of the features in low-confidence regions,the bundles are adaptively weighted using a spatially-variant weighting function based on the confidence values of the involved energy terms.Therefore,in each frame,the weights of the energy items in each bundle are adapted to different situations and different regions of the same frame.Experiments show that the proposed method can improve the overall accuracy in challenging cases.We then verify the effectiveness of the proposed confidence-based adaptive weighting method using ablation studies and show that the proposed method overperforms the existing single-feature methods and multi-feature methods without adaptive weighting.展开更多
In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techni...In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techniques developed in the past decade,both conventional methods and deep learning methods.These methods are evaluated with detailed descriptions of selected representative works.Their advantages and disadvantages are summarized in terms of accuracy,complexity,and robustness to facial variations(expression,pose,occlusion,etc.).A review of 3D face databases is also provided,and a discussion of future research challenges and directions of the topic.展开更多
基金The authors would like to thank Zhang Dongdong for his great help in experiments. This work was supported by the National Natural Science Foundation of China （Grant No. 61602324）, the Scientific Research Project of Beijing Educational Committeen （KM201710028018）, the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University （BUAA-VR-17KF-12） and Beijing Advanced Innovation Center for Imaging Technology （BAlCIT-2016004）.
文摘3D model retrieval virtual reality applications. In can benefit many downstream this paper, we propose a new sketch-based 3D model retrieval framework by coupling local features and manifold ranking. At technical fronts, we exploit spatial pyramids based local structures to facilitate the efficient construction of feature descriptors. Meanwhile, we propose an improved manifold ranking method, wherein all the categories between arbitrary model pairs will be taken into account. Since the smooth and detail-preserving line drawings of 3D model are important for sketch-based 3D model retrieval, the Difference of Gaussians （DOG） method is employed to extract the line drawings over the projected depth images of 3D model, and Bezier Curve is then adopted to further optimize the extracted line drawing. On that basis, we develop a 3D model retrieval engine to verify our method. We have conducted extensive experiments over various public benchmarks, and have made comprehensive comparisons with some state-of-the-art 3D retrieval methods. All the evaluation results based on the widely-used indicators prove the superiority of our method in accuracy, reliability, robustness, and versatility.
基金supported by Zhejiang Lab under Grant No.2020NB0AB02the Industrial Internet Innovation and Development Project in 2019 of China。
文摘The 3D object tracking from a monocular RGB image is a challenging task.Although popular color and edgebased methods have been well studied,they are only applicable to certain cases and new solutions to the challenges in real environment must be developed.In this paper,we propose a robust 3D object tracking method with adaptively weighted local bundles called AWLB tracker to handle more complicated cases.Each bundle represents a local region containing a set of local features.To alleviate the negative effect of the features in low-confidence regions,the bundles are adaptively weighted using a spatially-variant weighting function based on the confidence values of the involved energy terms.Therefore,in each frame,the weights of the energy items in each bundle are adapted to different situations and different regions of the same frame.Experiments show that the proposed method can improve the overall accuracy in challenging cases.We then verify the effectiveness of the proposed confidence-based adaptive weighting method using ablation studies and show that the proposed method overperforms the existing single-feature methods and multi-feature methods without adaptive weighting.
文摘In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techniques developed in the past decade,both conventional methods and deep learning methods.These methods are evaluated with detailed descriptions of selected representative works.Their advantages and disadvantages are summarized in terms of accuracy,complexity,and robustness to facial variations(expression,pose,occlusion,etc.).A review of 3D face databases is also provided,and a discussion of future research challenges and directions of the topic.
