Object detection is a crucial branch of computer vision that aims to locate and classify
objects in images. Using deep convolutional neural networks (CNNs) as the primary …

In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

In this paper, we propose a long-sequence modeling framework, named StreamPETR, for
multi-view 3D object detection. Built upon the sparse query design in the PETR series, we …

Camera-based 3D object detection in BEV (Bird's Eye View) space has drawn great
attention over the past few years. Dense detectors typically follow a two-stage pipeline by …

Abstract View Transformation Module (VTM), where transformations happen between multi-
view image features and Bird-Eye-View (BEV) representation, is a crucial step in camera …

Jointly processing information from multiple sensors is crucial to achieving accurate and
robust perception for reliable autonomous driving systems. However, current 3D perception …

In the context of autonomous driving the significance of effective feature learning is widely
acknowledged. While conventional 3D self-supervised pre-training methods have shown …

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant
interest from both industry and academia due to its inherent advantages, such as providing …

Abstract We introduce Deformable Convolution v4 (DCNv4) a highly efficient and effective
operator designed for a broad spectrum of vision applications. DCNv4 addresses the …

End-to-end autonomous driving recently emerged as a promising research direction to
target autonomy from a full-stack perspective. Along this line many of the latest works follow …