We propose to jointly learn multi-view geometry and warping between views of
the same object instances for robust cross-view object detection. What makes
multi-view object instance detection difficult are strong changes in viewpoint,
lighting conditions, high similarity of neighbouring objects, and strong
variability in scale. By turning object detection and instance
re-identification in different views into a joint learning task, we are able to
incorporate both image appearance and geometric soft constraints into a single,
multi-view detection process that is learnable end-to-end. We validate our
method on a new, large data set of street-level panoramas of urban objects and
show superior performance compared to various baselines. Our contribution is
threefold: a large-scale, publicly available data set for multi-view instance
detection and re-identification; an annotation tool custom-tailored for
multi-view instance detection; and a novel, holistic multi-view instance
detection and re-identification method that jointly models geometry and
appearance across views.

本文提出一种综合学习多视图几何和视图间扭曲的技术，适用于鲁棒的跨视图物体检测，结合图像外貌和几何学软约束的单一多视图检测过程可通过新的大尺度公开数据集进行验证，在城市全景下呈现出优秀性能，具有跨视图物体识别和重新识别、定制化多视图实例检测的注解工具和新的综合多视图检测和重新识别多种贡献。