Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

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 …

Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

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 …

Abstract We present NeRF-Det, a novel method for indoor 3D detection with posed RGB
images as input. Unlike existing indoor 3D detection methods that struggle to model scene …

Autonomous driving requires an accurate and fast 3D perception system that includes 3D
object detection, tracking, and segmentation. Although recent low-cost camera-based …

While most recent autonomous driving system focuses on developing perception methods
on ego-vehicle sensors, people tend to overlook an alternative approach to leverage …

In this paper, we propose a new paradigm, named Historical Object Prediction (HoP) for
multi-view 3D detection to leverage temporal information more effectively. The HoP …