Zobrazeno 1 - 10
of 271
pro vyhledávání: '"Huang, Zehao"'
The rise of autonomous vehicles has significantly increased the demand for robust 3D object detection systems. While cameras and LiDAR sensors each offer unique advantages--cameras provide rich texture information and LiDAR offers precise 3D spatial
Externí odkaz:
http://arxiv.org/abs/2408.05945
3D lane detection and topology reasoning are essential tasks in autonomous driving scenarios, requiring not only detecting the accurate 3D coordinates on lane lines, but also reasoning the relationship between lanes and traffic elements. Current visi
Externí odkaz:
http://arxiv.org/abs/2406.03105
Autor:
Huang, Zehao, Zhu, Gancheng, Duan, Xiaoting, Wang, Rong, Li, Yongkai, Zhang, Shuai, Wang, Zhiguo
With built-in eye-tracking cameras, the recently released Apple Vision Pro (AVP) mixed reality (MR) headset features gaze-based interaction, eye image rendering on external screens, and iris recognition for device unlocking. One of the technological
Externí odkaz:
http://arxiv.org/abs/2406.00255
Multi-modal 3D object detection has exhibited significant progress in recent years. However, most existing methods can hardly scale to long-range scenarios due to their reliance on dense 3D features, which substantially escalate computational demands
Externí odkaz:
http://arxiv.org/abs/2403.10036
Time-to-Contact (TTC) estimation is a critical task for assessing collision risk and is widely used in various driver assistance and autonomous driving systems. The past few decades have witnessed development of related theories and algorithms. The p
Externí odkaz:
http://arxiv.org/abs/2309.01539
Data association is a knotty problem for 2D Multiple Object Tracking due to the object occlusion. However, in 3D space, data association is not so hard. Only with a 3D Kalman Filter, the online object tracker can associate the detections from LiDAR.
Externí odkaz:
http://arxiv.org/abs/2306.05416
Currently prevalent multimodal 3D detection methods are built upon LiDAR-based detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature maps is quadratic to the detection range, making it not suitable
Externí odkaz:
http://arxiv.org/abs/2304.12310
Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. however, current data association solutions have some defects: they mostly ignore the intra-view context info
Externí odkaz:
http://arxiv.org/abs/2303.15414
Autor:
Huang, Shaofei, Shen, Zhenwei, Huang, Zehao, Ding, Zi-han, Dai, Jiao, Han, Jizhong, Wang, Naiyan, Liu, Si
Monocular 3D lane detection is a challenging task due to its lack of depth information. A popular solution is to first transform the front-viewed (FV) images or features into the bird-eye-view (BEV) space with inverse perspective mapping (IPM) and de
Externí odkaz:
http://arxiv.org/abs/2301.02371
3D object detection from multi-view images has drawn much attention over the past few years. Existing methods mainly establish 3D representations from multi-view images and adopt a dense detection head for object detection, or employ object queries d
Externí odkaz:
http://arxiv.org/abs/2301.02364