1.4k citations · 1.9k across the 39 of their papers we have counts for
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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…