Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit

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CenDerNet: Center and?Curvature Representations for?Render-and-Compare 6D Pose Estimationers; Third, 6D object poses are estimated using 3D centers and curvature heatmaps. By jointly optimizing poses across views using a render-and-compare approach, our method naturally handles occlusions and object symmetries. We show that CenDerNet outperforms previous methods on two industry-relevant datasets: DIMO and T-LESS.

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YORO - Lightweight End to?End Visual Groundingan object referred via natural language. Unlike the recent trend in the literature of using multi-stage approaches that sacrifice speed for accuracy, YORO seeks a better trade-off between speed an accuracy by embracing a single-stage design, without CNN backbone. YORO consumes natural language queri

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Localization Uncertainty Estimation for?Anchor-Free Object Detectionete data, it is desirable for object detectors to take the localization uncertainty into account. However, there are several limitations of the existing uncertainty estimation methods for anchor-based object detection. 1) They model the uncertainty of the heterogeneous object properties with differe

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Variational Depth Networks: Uncertainty-Aware Monocular Self-supervised Depth Estimationthey are susceptible to input ambiguities and it is therefore important to express the corresponding depth uncertainty. While there are a few truly monocular and self-supervised methods modelling uncertainty, none correlates well with errors in depth. To this end we present Variational Depth Network

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Unsupervised Joint Image Transfer and?Uncertainty Quantification Using Patch Invariant Networksundant. To ensure a structure-preserving mapping from the input to the target domain, existing methods for unpaired image transfer are commonly based on cycle-consistency, causing additional computational resources and instability due to the learning of an inverse mapping. This paper presents a nove

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Trans6D: Transformer-Based 6D Object Pose Estimation and?Refinementl network (CNN)-based methods have made remarkable progress, they are not efficient in capturing global dependencies and often suffer from information loss due to downsampling operations. To extract robust feature representation, we propose a Transformer-based 6D object pose estimation approach (Tra

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