Zobrazeno 1 - 10
of 6 059
pro vyhledávání: '"Ming, C."'
Autor:
Jacobson, Philip, Xie, Yichen, Ding, Mingyu, Xu, Chenfeng, Tomizuka, Masayoshi, Zhan, Wei, Wu, Ming C.
Semi-supervised 3D object detection is a common strategy employed to circumvent the challenge of manually labeling large-scale autonomous driving perception datasets. Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student fram
Externí odkaz:
http://arxiv.org/abs/2409.10901
3D-free meets 3D priors: Novel View Synthesis from a Single Image with Pretrained Diffusion Guidance
Recent 3D novel view synthesis (NVS) methods are limited to single-object-centric scenes and struggle with complex environments. They often require extensive 3D data for training, lacking generalization beyond the training distribution. Conversely, 3
Externí odkaz:
http://arxiv.org/abs/2408.06157
Autor:
Thalapanane, Sandeep, Kumar, Sandip Sharan Senthil, Peethambari, Guru Nandhan Appiya Dilipkumar, SriHari, Sourang, Zheng, Laura, Poveda, Julio, Lin, Ming C.
Data for training learning-enabled self-driving cars in the physical world are typically collected in a safe, normal environment. Such data distribution often engenders a strong bias towards safe driving, making self-driving cars unprepared when enco
Externí odkaz:
http://arxiv.org/abs/2407.09466
Vector fields are widely used to represent and model flows for many science and engineering applications. This paper introduces a novel neural network architecture for learning tangent vector fields that are intrinsically defined on manifold surfaces
Externí odkaz:
http://arxiv.org/abs/2406.09648
In trajectory forecasting tasks for traffic, future output trajectories can be computed by advancing the ego vehicle's state with predicted actions according to a kinematics model. By unrolling predicted trajectories via time integration and models o
Externí odkaz:
http://arxiv.org/abs/2406.01431
Autor:
Zheng, Laura, Wei, Wenjie, Wu, Tony, Clements, Jacob, Revankar, Shreelekha, Harrison, Andre, Shen, Yu, Lin, Ming C.
Segmentation is an integral module in many visual computing applications such as virtual try-on, medical imaging, autonomous driving, and agricultural automation. These applications often involve either widespread consumer use or highly variable envi
Externí odkaz:
http://arxiv.org/abs/2406.01425
We present a differentiable representation, DMesh, for general 3D triangular meshes. DMesh considers both the geometry and connectivity information of a mesh. In our design, we first get a set of convex tetrahedra that compactly tessellates the domai
Externí odkaz:
http://arxiv.org/abs/2404.13445
We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm. To incorporate analytical gradients into the PPO framework, we introduce the conc
Externí odkaz:
http://arxiv.org/abs/2312.08710
Collaborative decision-making is an essential capability for multi-robot systems, such as connected vehicles, to collaboratively control autonomous vehicles in accident-prone scenarios. Under limited communication bandwidth, capturing comprehensive s
Externí odkaz:
http://arxiv.org/abs/2310.20491
Autor:
Liang, Jing, Gao, Peng, Xiao, Xuesu, Sathyamoorthy, Adarsh Jagan, Elnoor, Mohamed, Lin, Ming C., Manocha, Dinesh
We present a novel learning-based trajectory generation algorithm for outdoor robot navigation. Our goal is to compute collision-free paths that also satisfy the environment-specific traversability constraints. Our approach is designed for global pla
Externí odkaz:
http://arxiv.org/abs/2309.08214