Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Ma, Yukai"'
Monocular Event-Inertial Odometry with Adaptive decay-based Time Surface and Polarity-aware Tracking
Event cameras have garnered considerable attention due to their advantages over traditional cameras in low power consumption, high dynamic range, and no motion blur. This paper proposes a monocular event-inertial odometry incorporating an adaptive de
Externí odkaz:
http://arxiv.org/abs/2409.13971
Autor:
Mei, Jianbiao, Ma, Yukai, Yang, Xuemeng, Wen, Licheng, Wei, Tiantian, Dou, Min, Shi, Botian, Liu, Yong
Recent advances in diffusion models have significantly enhanced the cotrollable generation of streetscapes for and facilitated downstream perception and planning tasks. However, challenges such as maintaining temporal coherence, generating long video
Externí odkaz:
http://arxiv.org/abs/2409.04003
Autor:
Yang, Yu, Mei, Jianbiao, Ma, Yukai, Du, Siliang, Chen, Wenqing, Qian, Yijie, Feng, Yuxiang, Liu, Yong
World models envision potential future states based on various ego actions. They embed extensive knowledge about the driving environment, facilitating safe and scalable autonomous driving. Most existing methods primarily focus on either data generati
Externí odkaz:
http://arxiv.org/abs/2408.14197
Autor:
Yang, Xuemeng, Wen, Licheng, Ma, Yukai, Mei, Jianbiao, Li, Xin, Wei, Tiantian, Lei, Wenjie, Fu, Daocheng, Cai, Pinlong, Dou, Min, Shi, Botian, He, Liang, Liu, Yong, Qiao, Yu
This paper presented DriveArena, the first high-fidelity closed-loop simulation system designed for driving agents navigating in real scenarios. DriveArena features a flexible, modular architecture, allowing for the seamless interchange of its core c
Externí odkaz:
http://arxiv.org/abs/2408.00415
Autor:
Ma, Yukai, Mei, Jianbiao, Yang, Xuemeng, Wen, Licheng, Xu, Weihua, Zhang, Jiangning, Shi, Botian, Liu, Yong, Zuo, Xingxing
Semantic Scene Completion (SSC) is pivotal in autonomous driving perception, frequently confronted with the complexities of weather and illumination changes. The long-term strategy involves fusing multi-modal information to bolster the system's robus
Externí odkaz:
http://arxiv.org/abs/2407.16197
Autor:
Mei, Jianbiao, Ma, Yukai, Yang, Xuemeng, Wen, Licheng, Cai, Xinyu, Li, Xin, Fu, Daocheng, Zhang, Bo, Cai, Pinlong, Dou, Min, Shi, Botian, He, Liang, Liu, Yong, Qiao, Yu
Autonomous driving has advanced significantly due to sensors, machine learning, and artificial intelligence improvements. However, prevailing methods struggle with intricate scenarios and causal relationships, hindering adaptability and interpretabil
Externí odkaz:
http://arxiv.org/abs/2405.15324
Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning. Adverse weather conditions, including haze, dust, rain, snow, and darkness, introduce signi
Externí odkaz:
http://arxiv.org/abs/2402.02067
We present a novel approach for metric dense depth estimation based on the fusion of a single-view image and a sparse, noisy Radar point cloud. The direct fusion of heterogeneous Radar and image data, or their encodings, tends to yield dense depth ma
Externí odkaz:
http://arxiv.org/abs/2401.04325
Autor:
Mei, Jianbiao, Yang, Yu, Wang, Mengmeng, Zhu, Junyu, Ra, Jongwon, Ma, Yukai, Li, Laijian, Liu, Yong
Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for autonomous driving. Recently, many studies have turned to camera-based S
Externí odkaz:
http://arxiv.org/abs/2312.05752
The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N consecutive weights along the output channel to be group-wise non-zero, th
Externí odkaz:
http://arxiv.org/abs/2310.06218