17 citations · 17 across the 11 of their papers we have counts for
13 papers
EnerGS: Energy-Based Gaussian Splatting with Partial Geometric Priors
Rui Song, Tianhui Cai, Markus Gross +5
3D Gaussian Splatting (3DGS) has been widely adopted for scene reconstruction, where training inherently constitutes a highly coupled and non-convex optimization problem. Recent wo…
SGTA: Scene-Graph Based Multi-Modal Traffic Agent for Video Understanding
Xingcheng Zhou, Mingyu Liu, Walter Zimmer +2
We present Scene-Graph Based Multi-Modal Traffic Agent (SGTA), a modular framework for traffic video understanding that combines structured scene graphs with multi-modal reasoning.…
SegRGB-X: General RGB-X Semantic Segmentation Model
Jiong Liu, Yingjie Xu, Xingcheng Zhou +4
Semantic segmentation across arbitrary sensor modalities faces significant challenges due to diverse sensor characteristics, and the traditional configurations for this task result…
Energy-Aware Imitation Learning for Steering Prediction Using Events and Frames
Hu Cao, Jiong Liu, Xingzhuo Yan +5
In autonomous driving, relying solely on frame-based cameras can lead to inaccuracies caused by factors like long exposure times, high-speed motion, and challenging lighting condit…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving
Rui Song, Chenwei Liang, Yan Xia +5
Dynamic scene rendering opens new avenues in autonomous driving by enabling closed-loop simulations with photorealistic data, which is crucial for validating end-to-end algorithms.…
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
Xingcheng Zhou, Konstantinos Larintzakis, Hao Guo +7
We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. The dataset comprises 1,000 vid…
Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection
Sondos Mohamed, Walter Zimmer, Ross Greer +6
Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditi…
WARM-3D: A Weakly-Supervised Sim2Real Domain Adaptation Framework for Roadside Monocular 3D Object Detection
Xingcheng Zhou, Deyu Fu, Walter Zimmer +4
Existing roadside perception systems are limited by the absence of publicly available, large-scale, high-quality 3D datasets. Exploring the use of cost-effective, extensive synthet…
GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs
Mingyu Liu, Ekim Yurtsever, Marc Brede +6
Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibit…
Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles
Rui Song, Chenwei Liang, Hu Cao +5
Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D pe…
Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior
Xinyi Li, Zijian Ma, Yinlong Liu +4
Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with…
Vision Language Models in Autonomous Driving: A Survey and Outlook
Xingcheng Zhou, Mingyu Liu, Ekim Yurtsever +4
The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD) have attracted widespread attention due to their outstanding performance and the ability t…