227 citations · 270 across the 12 of their papers we have counts for
12 papers
GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous Driving
Zhangshuo Qi, Junyi Ma, Jingyi Xu +3
Place recognition is a crucial component that enables autonomous vehicles to obtain localization results in GPS-denied environments. In recent years, multimodal place recognition m…
Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric Videos
Junyi Ma, Jingyi Xu, Xieyuanli Chen +1
Understanding how humans would behave during hand-object interaction is vital for applications in service robot manipulation and extended reality. To achieve this, some recent work…
Explicit Interaction for Fusion-Based Place Recognition
Jingyi Xu, Junyi Ma, Qi Wu +4
Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and…
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments
Xiuzhong Hu, Guangming Xiong, Zheng Zang +3
Large-scale 3D scene reconstruction and novel view synthesis are vital for autonomous vehicles, especially utilizing temporally sparse LiDAR frames. However, conventional explicit…
LCPR: A Multi-Scale Attention-Based LiDAR-Camera Fusion Network for Place Recognition
Zijie Zhou, Jingyi Xu, Guangming Xiong +1
Place recognition is one of the most crucial modules for autonomous vehicles to identify places that were previously visited in GPS-invalid environments. Sensor fusion is considere…
Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications
Junyi Ma, Xieyuanli Chen, Jiawei Huang +6
Understanding how the surrounding environment changes is crucial for performing downstream tasks safely and reliably in autonomous driving applications. Recent occupancy estimation…
PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments
Xiuzhong Hu, Guangming Xiong, Zheng Zang +3
Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost. Although the recently developed neural radiance fields (NeRF…
PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction
Zhen Luo, Junyi Ma, Zijie Zhou +1
The ability to predict future structure features of environments based on past perception information is extremely needed by autonomous vehicles, which helps to make the following…
CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data
Junyi Ma, Guangming Xiong, Jingyi Xu +1
LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous vehicles to identify previously visited places in GPS-denied environments. Most existing LPR…
SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data
Junyi Ma, Xieyuanli Chen, Jingyi Xu +1
Place recognition is an important component for autonomous vehicles to achieve loop closing or global localization. In this paper, we tackle the problem of place recognition based…
OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition
Junyi Ma, Jun Zhang, Jintao Xu +3
Place recognition is an important capability for autonomously navigating vehicles operating in complex environments and under changing conditions. It is a key component for tasks s…
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles
Jingyi Xu, Zirui Li, Li Gao +3
The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the…