3D occupancy, an advanced perception technology for driving scenarios,
represents the entire scene without distinguishing between foreground and
background by quantifying the physical space into a grid map. The widely
adopted projection-first deformable attention, efficient in transforming image
features into 3D representations, encounters challenges in aggregating
multi-view features due to sensor deployment constraints. To address this
issue, we propose our learning-first view attention mechanism for effective
multi-view feature aggregation. Moreover, we showcase the scalability of our
view attention across diverse multi-view 3D tasks, such as map construction and
3D object detection. Leveraging the proposed view attention as well as an
additional multi-frame streaming temporal attention, we introduce ViewFormer, a
vision-centric transformer-based framework for spatiotemporal feature
aggregation. To further explore occupancy-level flow representation, we present
FlowOcc3D, a benchmark built on top of existing high-quality datasets.
Qualitative and quantitative analyses on this benchmark reveal the potential to
represent fine-grained dynamic scenes. Extensive experiments show that our
approach significantly outperforms prior state-of-the-art methods. The codes
and benchmark will be released soon.

基于 3D 占据率、多视图特征聚合和动态场景的研究，我们提出了 ViewFormer，一个基于 Transformer 的视觉中心框架，具有高度的可扩展性和优越性能。

ViewFormer：基于视图引导的 Transformer 模型探索多视角 3D 占位感知的时空建模

ViewFormer: Exploring Spatiotemporal Modeling for Multi-View 3D  Occupancy Perception via View-Guided Transformers

Driven by deep learning techniques, perception technology in autonomous
driving has developed rapidly in recent years. To achieve accurate and robust
perception capabilities, autonomous vehicles are often equipped with multiple
sensors, making sensor fusion a crucial part of the perception system. Among
these fused sensors, radars and cameras enable a complementary and
cost-effective perception of the surrounding environment regardless of lighting
and weather conditions. This review aims to provide a comprehensive guideline
for radar-camera fusion, particularly concentrating on perception tasks related
to object detection and semantic segmentation. Based on the principles of the
radar and camera sensors, we delve into the data processing process and
representations, followed by an in-depth analysis and summary of radar-camera
fusion datasets. In the review of methodologies in radar-camera fusion, we
address interrogative questions, including "why to fuse", "what to fuse",
"where to fuse", "when to fuse", and "how to fuse", subsequently discussing
various challenges and potential research directions within this domain. To
ease the retrieval and comparison of datasets and fusion methods, we also
provide an interactive website:
this https URL

本论文评述了无人驾驶中基于深度学习的感知技术的发展，介绍了传感器融合技术在提升高精度和鲁棒性感知能力中的关键地位，重点介绍了雷达和相机传感器融合在物体检测和语义分割问题中，探索了该领域中的各种挑战和潜在的研究方向以及数据集和融合方法的互动网站。