Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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SpatialDETR: Robust Scalable Transformer-Based 3D Object Detection From Multi-view Camera Images Wixploits arbitrary receptive fields to integrate cross-sensor data and therefore global context. Extensive experiments on the nuScenes benchmark demonstrate the potential of global attention and result in state-of-the-art performance. Code available at ..

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憤慨一下

,PreTraM: Self-supervised Pre-training via?Connecting Trajectory and?Map,ctories and maps to a shared embedding space with cross-modal contrastive learning, 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM reduces their error

遺傳學(xué)

,Master of?All: Simultaneous Generalization of?Urban-Scene Segmentation to?, Adverse Weather Conditi, given a pre-trained model and its parameters, . enforces edge consistency prior at the inference stage and updates the model based on (a) a single test sample at a time (.), or (b) continuously for the whole test domain (.). Not only the target data, . also does not need access to the source data

缺乏

,LESS: Label-Efficient Semantic Segmentation for?LiDAR Point Clouds,g step, we leverage prototype learning to get more descriptive point embeddings and use multi-scan distillation to exploit richer semantics from temporally aggregated point clouds to boost the performance of single-scan models. Evaluated on the SemanticKITTI and the nuScenes datasets, we show that o

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,Visual Cross-View Metric Localization with?Dense Uncertainty Estimates,e compare against a state-of-the-art regression baseline that uses global image descriptors. Quantitative and qualitative experimental results on the recently proposed VIGOR and the Oxford RobotCar datasets validate our design. The produced probabilities are correlated with localization accuracy, an

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Nonconformist

,DevNet: Self-supervised Monocular Depth Learning via?Density Volume Construction,sponding rays. During the training process, novel regularization strategies and loss functions are introduced to mitigate photometric ambiguities and overfitting. Without obviously enlarging model parameters size or running time, DevNet outperforms several representative baselines on both the KITTI-