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 …

We present a novel bird's-eye-view (BEV) detector with perspective supervision, which
converges faster and better suits modern image backbones. Existing state-of-the-art BEV …

While recent camera-only 3D detection methods leverage multiple timesteps, the limited
history they use significantly hampers the extent to which temporal fusion can improve object …

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 …

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 …

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 …

Long-term temporal fusion is a crucial but often overlooked technique in camera-based
Bird's-Eye-View (BEV) 3D perception. Existing methods are mostly in a parallel manner …

This paper proposes an efficient multi-camera to Bird's-Eye-View (BEV) view transformation
method for 3D perception, dubbed MatrixVT. Existing view transformers either suffer from …