论文标题
统一：统一的多视图融合变压器，用于鸟类视图中的时空表示
UniFusion: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird's-Eye-View
论文作者
论文摘要
Bird's Eye View（BEV）表示是一种基于空间融合的自动驾驶的新知觉公式。此外，在BEV表示中还引入了时间融合并获得了巨大的成功。在这项工作中，我们提出了一种统一空间和时间融合的新方法，并将它们合并为统一的数学公式。统一的融合不仅可以为BEV融合提供新的观点，而且还可以带来新的功能。借助拟议的统一时空融合，我们的方法可以支持远程融合，这在常规的BEV方法中很难实现。此外，我们工作中的BEV融合是时间自适应的，时间融合的权重是可以学习的。相比之下，常规方法主要使用固定权重和相等的权重进行时间融合。此外，拟议的统一融合可以避免在常规的BEV融合方法中丢失的信息，并充分利用功能。对Nuscenes数据集进行的广泛实验和消融研究表明，该方法的有效性，我们的方法在MAP分割任务中获得了最新性能。
Bird's eye view (BEV) representation is a new perception formulation for autonomous driving, which is based on spatial fusion. Further, temporal fusion is also introduced in BEV representation and gains great success. In this work, we propose a new method that unifies both spatial and temporal fusion and merges them into a unified mathematical formulation. The unified fusion could not only provide a new perspective on BEV fusion but also brings new capabilities. With the proposed unified spatial-temporal fusion, our method could support long-range fusion, which is hard to achieve in conventional BEV methods. Moreover, the BEV fusion in our work is temporal-adaptive and the weights of temporal fusion are learnable. In contrast, conventional methods mainly use fixed and equal weights for temporal fusion. Besides, the proposed unified fusion could avoid information lost in conventional BEV fusion methods and make full use of features. Extensive experiments and ablation studies on the NuScenes dataset show the effectiveness of the proposed method and our method gains the state-of-the-art performance in the map segmentation task.
