A Novel 3D River Basin Sketching Process for Unconstrained 3D Object Detection and Tracking


A Novel 3D River Basin Sketching Process for Unconstrained 3D Object Detection and Tracking – We present a deep learning based framework for 3D reconstruction of high dynamic range (HDR) objects from an unsupervised way, that can be trained as an ensemble of an RGB-D, stereo-enhanced and multi-resolution 3D models. The proposed framework is first formulated as a 3D model that trains independently for reconstruction and tracking of HDR objects. Using a deep learning architecture to perform the final reconstruction, the proposed framework can learn the 3D predictions of HDR objects (in terms of relative tracking accuracy, relative pose and pose-related motion), and adapt to the local 3D model’s pose and pose-related features as well as the 2D model’s 3D poses. Our framework is a fully convolutional approach that is flexible on multiple 3D reconstruction tasks. Our method achieves state-of-the-art performance for HDR object retrieval based on a 2D model on different tasks.

We present the first approach that uses a neural network to learn a structured embeddings of complex input data without any prior supervision. The embedding consists of a structure over different classes of variables: variables in the input data can be either labelled as continuous variables or variable names can be generated by neural networks. Experiments show that the embedding model is able to extract such structure, i.e. we can infer how the complex data might fit in a structured model without making any pre-processing steps.

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A Novel 3D River Basin Sketching Process for Unconstrained 3D Object Detection and Tracking

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    Towards Optimal Cooperative and Efficient Hardware ImplementationsWe present the first approach that uses a neural network to learn a structured embeddings of complex input data without any prior supervision. The embedding consists of a structure over different classes of variables: variables in the input data can be either labelled as continuous variables or variable names can be generated by neural networks. Experiments show that the embedding model is able to extract such structure, i.e. we can infer how the complex data might fit in a structured model without making any pre-processing steps.


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