Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

Harbor

https://doi.org/10.1007/978-1-349-24924-4e created an annotated dataset and benchmarked seven state-of-the-art deep learning classification methods in three categories, namely: (1) point clouds, (2) volumetric representation in voxel grids, and (3) view-based representation.

聽寫

https://doi.org/10.1007/978-1-349-24924-4ated garment model can be easily retargeted to another body, enabling garment customization. In addition, a large garment appearance dataset is provided for use in garment reconstruction, garment capturing, and other applications. We demonstrate that our generative model has high reconstruction accu

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現(xiàn)代

https://doi.org/10.1007/978-1-349-24924-4nd other multi-frame approaches by 1.2% while using less memory and computation per frame. To the best of our knowledge, this is the first work to use an LSTM for 3D object detection in sparse point clouds.

革新

https://doi.org/10.1007/978-1-349-24924-4sed Co-Attention assisted ranking network shows superior performance even over the supervised(The term “supervised” refers to the approach with access to the manual ground-truth annotations for training.) approach. The effectiveness of our Contrastive Attention module is also demonstrated by the per