2 citations · 3 across the 15 of their papers we have counts for
8 papers · 1 filter
XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments
Kangan Qian, ChuChu Xie, Yang Zhong +13
Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from complex environments. Current cloud…
SwiftGS: Episodic Priors for Immediate Satellite Surface Recovery
Rong Fu, Jiekai Wu, Xiaowen Ma +4
Rapid, large-scale 3D reconstruction from multi-date satellite imagery is vital for environmental monitoring, urban planning, and disaster response, yet remains difficult due to il…
Smooth Operator: Smooth Verifiable Reward Activates Spatial Reasoning Ability of Vision-Language Model
Siwen Jiao, Tianxiong Lv, Kangan Qian +7
Vision-Language Models (VLMs) face a critical bottleneck in achieving precise numerical prediction for 3D scene understanding. Traditional reinforcement learning (RL) approaches, p…
A Survey on Vision-Language-Action Models for Autonomous Driving
Sicong Jiang, Zilin Huang, Kangan Qian +17
The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language unde…
POD: Predictive Object Detection with Single-Frame FMCW LiDAR Point Cloud
Yining Shi, Kun Jiang, Xin Zhao +5
LiDAR-based 3D object detection is a fundamental task in the field of autonomous driving. This paper explores the unique advantage of Frequency Modulated Continuous Wave (FMCW) LiD…
LEGO-Motion: Learning-Enhanced Grids with Occupancy Instance Modeling for Class-Agnostic Motion Prediction
Kangan Qian, Jinyu Miao, Ziang Luo +7
Accurate and reliable spatial and motion information plays a pivotal role in autonomous driving systems. However, object-level perception models struggle with handling open scenari…
How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic
Kangan Qian, Jinyu Miao, Xinyu Jiao +6
Understanding the spatial dynamics of cars within urban systems is essential for optimizing infrastructure management and resource allocation. Recent empirical approaches for analy…
EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving
Yining Shi, Kun Jiang, Jinyu Miao +8
3D occupancy prediction (3DOcc) is a rapidly rising and challenging perception task in the field of autonomous driving. Existing 3D occupancy networks (OccNets) are both computatio…