Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

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 …

Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training
(CLIP) have shown inspirational performance on 2D visual recognition, which learns to …

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This
representation mimics the well-studied image-based 2D bounding-box detection but comes …

Prevalence of voxel-based 3D single-stage detectors contrast with underexplored point-
based methods. In this paper, we present a lightweight point-based 3D single stage object …

Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

LiDAR-based 3D detection in point cloud is essential in the perception system of
autonomous driving. In this paper, we present LiDAR R-CNN, a second stage detector that …

We propose a two-stage 3D object detection framework, named sparse-to-dense 3D Object
Detector (STD). The first stage is a bottom-up proposal generation network that uses raw …

In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities
with complementary cues for 3D object detection. However, it is quite difficult to sufficiently …