Zero-BEV：任意第一人称模态到BEV地图的零样本投影
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps
AI总结：
提出Zero-BEV模型，通过解耦几何逆透视投影与模态转换，实现任意第一人称模态到BEV地图的零样本投影，并在多种模态上优于依赖单目深度估计的基线方法。
摘要：
鸟瞰图（BEV）地图是一种重要的几何结构化表示，广泛应用于机器人领域，尤其是自动驾驶车辆和陆地机器人。现有算法要么需要深度信息来进行几何投影（这并非总是可靠获取的），要么以完全监督的端到端方式进行训练，将视觉第一人称观测映射到BEV表示，因此受限于其训练时所针对的输出模态。相比之下，我们提出了一种新模型，能够将第一人称视角下可用的任意模态零样本投影到相应的BEV地图。这是通过将几何逆透视投影与模态转换（例如RGB到占用率）解耦来实现的。该方法具有通用性，我们展示了将三种不同模态投影到BEV的实验：语义分割、运动向量以及在第一人称中检测到的目标边界框。我们通过实验证明，该模型优于竞争方法，尤其是依赖于单目深度估计的广泛使用基线。
英文摘要：
Bird's-eye view (BEV) maps are an important geometrically structured representation widely used in robotics, in particular self-driving vehicles and terrestrial robots. Existing algorithms either require depth information for the geometric projection, which is not always reliably available, or are trained end-to-end in a fully supervised way to map visual first-person observations to BEV representation, and are therefore restricted to the output modality they have been trained for. In contrast, we propose a new model capable of performing zero-shot projections of any modality available in a first person view to the corresponding BEV map. This is achieved by disentangling the geometric inverse perspective projection from the modality transformation, eg. RGB to occupancy. The method is general and we showcase experiments projecting to BEV three different modalities: semantic segmentation, motion vectors and object bounding boxes detected in first person. We experimentally show that the model outperforms competing methods, in particular the widely used baseline resorting to monocular depth estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
