1.4k citations · 1.9k across the 39 of their papers we have counts for
51 papers
MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts
Matthias Neuwirth-Trapp, Maarten Bieshaar, Danda Paudel +3
Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the c…
OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
Damiano Da Col, Maximilian Igl, Peter Karkus +5
As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as autonomous driving. End-to-end dr…
Robust Promptable Video Object Segmentation
Sohyun Lee, Yeho Gwon, Lukas Hoyer +3
The performance of promptable video object segmentation (PVOS) models substantially degrades under input corruptions, which prevents PVOS deployment in safety-critical domains. Thi…
Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark
Yiting Wang, Nolwenn Peyratout, Tim Brodermann +13
This paper presents the report of the URVIS 2026 challenge on adverse-to-extreme panoptic segmentation. As the first challenge of its kind, it attracted 17 registered participants…
AURORA-KITTI: Any-Weather Depth Completion and Denoising
Yiting Wang, Tim Brödermann, Hamed Haghighi +4
Robust depth completion is fundamental to real-world 3D scene understanding, yet existing RGB--LiDAR fusion methods degrade significantly under adverse weather, where both camera i…
Video Depth Propagation
Luigi Piccinelli, Thiemo Wandel, Christos Sakaridis +2
Depth estimation in videos is essential for visual perception in real-world applications. However, existing methods either rely on simple frame-by-frame monocular models, leading t…
DGFusion: Depth-Guided Sensor Fusion for Robust Semantic Perception
Tim Broedermannn, Christos Sakaridis, Luigi Piccinelli +2
Robust semantic perception for autonomous vehicles relies on effectively combining multiple sensors with complementary strengths and weaknesses. State-of-the-art sensor fusion appr…
You Only Train Once
Christos Sakaridis
The title of this paper is perhaps an overclaim. Of course, the process of creating and optimizing a learned model inevitably involves multiple training runs which potentially feat…
UniK3D: Universal Camera Monocular 3D Estimation
Luigi Piccinelli, Christos Sakaridis, Mattia Segu +4
Monocular 3D estimation is crucial for visual perception. However, current methods fall short by relying on oversimplified assumptions, such as pinhole camera models or rectified i…
UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler
Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang +4
Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is c…
PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields
Sean Wu, Shamik Basu, Tim Broedermann +2
We tackle the ill-posed inverse rendering problem in 3D reconstruction with a Neural Radiance Field (NeRF) approach informed by Physics-Based Rendering (PBR) theory, named PBR-NeRF…
Fine-Grained Spatial and Verbal Losses for 3D Visual Grounding
Sombit Dey, Ozan Unal, Christos Sakaridis +1
3D visual grounding consists of identifying the instance in a 3D scene which is referred by an accompanying language description. While several architectures have been proposed wit…
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
Tuan-Hung Vu, Eduardo Valle, Andrei Bursuc +16
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. W…
CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes
Tim Broedermann, Christos Sakaridis, Yuqian Fu +1
Leveraging multiple sensors is crucial for robust semantic perception in autonomous driving, as each sensor type has complementary strengths and weaknesses. However, existing senso…
Bayesian Self-Training for Semi-Supervised 3D Segmentation
Ozan Unal, Christos Sakaridis, Luc Van Gool
3D segmentation is a core problem in computer vision and, similarly to many other dense prediction tasks, it requires large amounts of annotated data for adequate training. However…
Optimizing against Infeasible Inclusions from Data for Semantic Segmentation through Morphology
Shamik Basu, Luc Van Gool, Christos Sakaridis
State-of-the-art semantic segmentation models are typically optimized in a data-driven fashion, minimizing solely per-pixel or per-segment classification objectives on their traini…
Sun Off, Lights On: Photorealistic Monocular Nighttime Simulation for Robust Semantic Perception
Konstantinos Tzevelekakis, Shutong Zhang, Luc Van Gool +1
Nighttime scenes are hard to semantically perceive with learned models and annotate for humans. Thus, realistic synthetic nighttime data become all the more important for learning…
TrafficBots V1.5: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding
Zhejun Zhang, Christos Sakaridis, Luc Van Gool
In this technical report we present TrafficBots V1.5, a baseline method for the closed-loop simulation of traffic agents. TrafficBots V1.5 achieves baseline-level performance and a…
Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar
David Borts, Erich Liang, Tim Brödermann +9
Neural fields have been broadly investigated as scene representations for the reproduction and novel generation of diverse outdoor scenes, including those autonomous vehicles and r…
UniDepth: Universal Monocular Metric Depth Estimation
Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis +4
Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is c…
Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes
Diandian Guo, Deng-Ping Fan, Tongyu Lu +2
The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes. Prio…
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
Tim Brödermann, David Bruggemann, Christos Sakaridis +4
Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse co…
MuRF: Multi-Baseline Radiance Fields
Haofei Xu, Anpei Chen, Yuedong Chen +5
We present Multi-Baseline Radiance Fields (MuRF), a general feed-forward approach to solving sparse view synthesis under multiple different baseline settings (small and large basel…
Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
Zhejun Zhang, Alexander Liniger, Christos Sakaridis +2
The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future…