17 citations · 33 across the 18 of their papers we have counts for
22 papers
Multimodal Fusion of RGB and Complementary Modalities for Semantic Segmentation
Hu Cao, Yifeng Cheng, Mu He +3
Multi-modal semantic segmentation adds an auxiliary sensing stream to RGB images (RGB-X), such as thermal, LiDAR, depth, event, polarization, or light field. This can improve robus…
DTFormer: Text-Guided Semantic Alignment for RGB-D Segmentation
Ziang Wei, Yinlong Liu, Yan Xia +2
RGB-D semantic segmentation has made notable progress by fusing RGB and Depth, yet mainstream models still learn features almost exclusively from pixel-level supervision, lacking d…
Efficient and Robust Absolute Pose Estimation via Gravity-Prior-Driven Transformation Decoupling and Pose Refinement
Hu Cao, Qianyi Yang, Xinyi Li +3
Estimation of the absolute pose of an object is an essential task for various robotic applications. Recently, incorporating gravity direction as prior information has emerged as a…
CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs
Xingcheng Zhou, Hao Guo, Rui Song +5
Safety-critical traffic reasoning requires contrastive consistency: models must detect true hazards when an accident occurs, and reliably reject plausible-but-false hypotheses unde…
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…
AffordanceGrasp-R1:Leveraging Reasoning-Based Affordance Segmentation with Reinforcement Learning for Robotic Grasping
Dingyi Zhou, Mu He, Zhuowei Fang +4
We introduce AffordanceGrasp-R1, a reasoning-driven affordance segmentation framework for robotic grasping that combines a chain-of-thought (CoT) cold-start strategy with reinforce…
Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulation
Zebin Jiang, Tianle Jin, Xiangtong Yao +2
Grasping is one of the most fundamental challenging capabilities in robotic manipulation, especially in unstructured, cluttered, and semantically diverse environments. Recent resea…
URNet: Uncertainty-aware Refinement Network for Event-based Stereo Depth Estimation
Yifeng Cheng, Alois Knoll, Hu Cao
Event cameras provide high temporal resolution, high dynamic range, and low latency, offering significant advantages over conventional frame-based cameras. In this work, we introdu…
Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset
Walter Zimmer, Ross Greer, Xingcheng Zhou +7
Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as an unavoidable…
BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models
Encheng Su, Hu Cao, Alois Knoll
Accurate segmentation of polyps and skin lesions is essential for diagnosing colorectal and skin cancers. While various segmentation methods for polyps and skin lesions using fully…
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.…
Towards Vision Zero: The TUM Traffic Accid3nD Dataset
Walter Zimmer, Ross Greer, Daniel Lehmberg +9
Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as unavoidable an…
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…
Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors
Walter Zimmer, Ross Greer, Xingcheng Zhou +7
Road traffic injuries are the leading cause of death for people aged 5-29, resulting in about 1.19 million deaths each year. To reduce these fatalities, it is essential to address…
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…
TUMTraf V2X Cooperative Perception Dataset
Walter Zimmer, Gerhard Arya Wardana, Suren Sritharan +3
Cooperative perception offers several benefits for enhancing the capabilities of autonomous vehicles and improving road safety. Using roadside sensors in addition to onboard sensor…
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…
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection
Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer +5
The curation of large-scale datasets is still costly and requires much time and resources. Data is often manually labeled, and the challenge of creating high-quality datasets remai…
TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras
Christian Creß, Walter Zimmer, Nils Purschke +5
Event-based cameras are predestined for Intelligent Transportation Systems (ITS). They provide very high temporal resolution and dynamic range, which can eliminate motion blur and…
Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning
Christoph Hümmer, Manuel Schwonberg, Liangwei Zhou +3
Domain generalization (DG) remains a significant challenge for perception based on deep neural networks (DNNs), where domain shifts occur due to synthetic data, lighting, weather,…
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…