Zobrazeno 1 - 10
of 987
pro vyhledávání: '"Pandey, Gaurav"'
Autonomous vehicles (AVs) heavily rely on LiDAR perception for environment understanding and navigation. LiDAR intensity provides valuable information about the reflected laser signals and plays a crucial role in enhancing the perception capabilities
Externí odkaz:
http://arxiv.org/abs/2404.15774
This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting. Our proposed method uses LiDAR and camera data to create accurate and visually plausible representations of the environment. By leveragi
Externí odkaz:
http://arxiv.org/abs/2403.11367
This research paper presents an innovative multi-task learning framework that allows concurrent depth estimation and semantic segmentation using a single camera. The proposed approach is based on a shared encoder-decoder architecture, which integrate
Externí odkaz:
http://arxiv.org/abs/2403.10662
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback
Autor:
Pandey, Gaurav, Nandwani, Yatin, Naseem, Tahira, Mishra, Mayank, Xu, Guangxuan, Raghu, Dinesh, Joshi, Sachindra, Munawar, Asim, Astudillo, Ramón Fernandez
Distribution matching methods for language model alignment such as Generation with Distributional Control (GDC) and Distributional Policy Gradient (DPG) have not received the same level of attention in reinforcement learning from human feedback (RLHF
Externí odkaz:
http://arxiv.org/abs/2402.02479
A continuous motion planning method for connected automated vehicles is considered for generating feasible trajectories in real-time using three consecutive clothoids. The proposed method reduces path planning to a small set of nonlinear algebraic eq
Externí odkaz:
http://arxiv.org/abs/2312.10880
Multimodal deep sensor fusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions. However, existing deep sensor fusion methods usually employ convoluted architectures with i
Externí odkaz:
http://arxiv.org/abs/2310.19372
Autor:
Kannapiran, Shenbagaraj, Bendapudi, Nalin, Yu, Ming-Yuan, Parikh, Devarth, Berman, Spring, Vora, Ankit, Pandey, Gaurav
Robust feature matching forms the backbone for most Visual Simultaneous Localization and Mapping (vSLAM), visual odometry, 3D reconstruction, and Structure from Motion (SfM) algorithms. However, recovering feature matches from texture-poor scenes is
Externí odkaz:
http://arxiv.org/abs/2308.01125
Autor:
Mishra, Subodh, Nagesh, Sushruth, Manglani, Sagar, Mills, Graham, Chakravarty, Punarjay, Pandey, Gaurav
This work describes the automatic registration of a large network (approximately 40) of fixed, ceiling-mounted environment cameras spread over a large area (approximately 800 squared meters) using a mobile calibration robot equipped with a single upw
Externí odkaz:
http://arxiv.org/abs/2208.07362
Autor:
Zou, Zhengxia, Zhang, Rusheng, Shen, Shengyin, Pandey, Gaurav, Chakravarty, Punarjay, Parchami, Armin, Liu, Henry X.
We propose a novel and pragmatic framework for traffic scene perception with roadside cameras. The proposed framework covers a full-stack of roadside perception pipeline for infrastructure-assisted autonomous driving, including object detection, obje
Externí odkaz:
http://arxiv.org/abs/2206.09770
Autor:
Rabinovich, Ella, Vetzler, Matan, Boaz, David, Kumar, Vineet, Pandey, Gaurav, Anaby-Tavor, Ateret
The rapidly growing market demand for automatic dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-oriented dialog systems. The success of these systems is highly dependent
Externí odkaz:
http://arxiv.org/abs/2204.05158