Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Cheng, Erkang"'
The field of autonomous driving has attracted considerable interest in approaches that directly infer 3D objects in the Bird's Eye View (BEV) from multiple cameras. Some attempts have also explored utilizing 2D detectors from single images to enhance
Externí odkaz:
http://arxiv.org/abs/2403.10353
Multi-camera-based 3D object detection has made notable progress in the past several years. However, we observe that there are cases (e.g. faraway regions) in which popular 2D object detectors are more reliable than state-of-the-art 3D detectors. In
Externí odkaz:
http://arxiv.org/abs/2403.06093
In autonomous driving, 3D lane detection using monocular cameras is an important task for various downstream planning and control tasks. Recent CNN and Transformer approaches usually apply a two-stage scheme in the model design. The first stage trans
Externí odkaz:
http://arxiv.org/abs/2402.06423
Publikováno v:
IEEE Robotics and Automation Letters (RAL), vol. 8, no. 12, pp. 8066-8073, 2023
Point cloud-based 3D object tracking is an important task in autonomous driving. Though great advances regarding Siamese-based 3D tracking have been made recently, it remains challenging to learn the correlation between the template and search branch
Externí odkaz:
http://arxiv.org/abs/2312.11051
Both CNN-based and Transformer-based object detection with bounding box representation have been extensively studied in computer vision and medical image analysis, but circular object detection in medical images is still underexplored. Inspired by th
Externí odkaz:
http://arxiv.org/abs/2308.16145
3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as i
Externí odkaz:
http://arxiv.org/abs/2209.07989
Exploration with sparse rewards remains a challenging research problem in reinforcement learning (RL). Especially for sequential object manipulation tasks, the RL agent always receives negative rewards until completing all sub-tasks, which results in
Externí odkaz:
http://arxiv.org/abs/2208.00843
Detection of rare objects (e.g., traffic cones, traffic barrels and traffic warning triangles) is an important perception task to improve the safety of autonomous driving. Training of such models typically requires a large number of annotated data wh
Externí odkaz:
http://arxiv.org/abs/2205.00376
Semantic segmentation in bird's eye view (BEV) is an important task for autonomous driving. Though this task has attracted a large amount of research efforts, it is still challenging to flexibly cope with arbitrary (single or multiple) camera sensors
Externí odkaz:
http://arxiv.org/abs/2203.04050
Robust and accurate localization is an essential component for robotic navigation and autonomous driving. The use of cameras for localization with high definition map (HD Map) provides an affordable localization sensor set. Existing methods suffer fr
Externí odkaz:
http://arxiv.org/abs/2107.02557