656 citations · 1.2k across the 22 of their papers we have counts for
22 papers
Invaria: Learning Scale and Density Invariance in Point Clouds via Next-Resolution Prediction
Chun-Peng Chang, Shaoxiang Wang, Alain Pagani +2
Modern image encoders achieve high generalization by decoupling semantic meaning from resolution, an ability yet to be fully realized in the 3D domain. We investigate the failure o…
TerraSeg: Self-Supervised Ground Segmentation for Any LiDAR
Ted Lentsch, Santiago Montiel-Marín, Holger Caesar +1
LiDAR perception is fundamental to robotics, enabling machines to understand their environment in 3D. A crucial task for LiDAR-based scene understanding and navigation is ground se…
CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning
Marcell Kegl, Andras Palffy, Csaba Benedek +1
In this paper, we address extrinsic calibration for camera, lidar, and 4D radar sensors. Accurate extrinsic calibration of radar remains a challenge due to the sparsity of its data…
DRIFT: Dual-Representation Inter-Fusion Transformer for Automated Driving Perception with 4D Radar Point Clouds
Siqi Pei, Andras Palffy, Dariu M. Gavrila
4D radars, which provide 3D point cloud data along with Doppler velocity, are attractive components of modern automated driving systems due to their low cost and robustness under a…
Multi-Modal Model Predictive Path Integral Control for Collision Avoidance
Alberto Bertipaglia, Dariu M. Gavrila, Barys Shyrokau
This paper proposes a novel approach to motion planning and decision-making for automated vehicles, using a multi-modal Model Predictive Path Integral control algorithm. The method…
A Vehicle System for Navigating Among Vulnerable Road Users Including Remote Operation
Oscar de Groot, Alberto Bertipaglia, Hidde Boekema +21
We present a vehicle system capable of navigating safely and efficiently around Vulnerable Road Users (VRUs), such as pedestrians and cyclists. The system comprises key modules for…
UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes
Ted Lentsch, Holger Caesar, Dariu M. Gavrila
Unsupervised 3D object detection methods have emerged to leverage vast amounts of data without requiring manual labels for training. Recent approaches rely on dynamic objects for l…
See Further Than CFAR: a Data-Driven Radar Detector Trained by Lidar
Ignacio Roldan, Andras Palffy, Julian F. P. Kooij +3
In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with mul…
Topology-Driven Parallel Trajectory Optimization in Dynamic Environments
Oscar de Groot, Laura Ferranti, Dariu M. Gavrila +1
Ground robots navigating in complex, dynamic environments must compute collision-free trajectories to avoid obstacles safely and efficiently. Nonconvex optimization is a popular me…
Multimodal Object Query Initialization for 3D Object Detection
Mathijs R. van Geerenstein, Felicia Ruppel, Klaus Dietmayer +1
3D object detection models that exploit both LiDAR and camera sensor features are top performers in large-scale autonomous driving benchmarks. A transformer is a popular network ar…
Scenario-Based Motion Planning with Bounded Probability of Collision
Oscar de Groot, Laura Ferranti, Dariu Gavrila +1
Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced i…
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
Fangqiang Ding, Andras Palffy, Dariu M. Gavrila +1
This work proposes a novel approach to 4D radar-based scene flow estimation via cross-modal learning. Our approach is motivated by the co-located sensing redundancy in modern auton…
Globally Guided Trajectory Planning in Dynamic Environments
O. de Groot, L. Ferranti, D. Gavrila +1
Navigating mobile robots through environments shared with humans is challenging. From the perspective of the robot, humans are dynamic obstacles that must be avoided. These obstacl…
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?
Thomas M. Hehn, Julian F. P. Kooij, Dariu M. Gavrila
Various state-of-the-art self-supervised visual representation learning approaches take advantage of data from multiple sensors by aligning the feature representations across views…
Structural Knowledge Distillation for Object Detection
Philip de Rijk, Lukas Schneider, Marius Cordts +1
Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student. KD has pr…
Scenario-Based Trajectory Optimization in Uncertain Dynamic Environments
O. de Groot, B. Brito, L. Ferranti +2
We present an optimization-based method to plan the motion of an autonomous robot under the uncertainties associated with dynamic obstacles, such as humans. Our method bounds the m…
Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data
Christoph B. Rist, David Emmerichs, Markus Enzweiler +1
Semantic scene completion is the task of jointly estimating 3D geometry and semantics of objects and surfaces within a given extent. This is a particularly challenging task on real…
Diversity in Action: General-Sum Multi-Agent Continuous Inverse Optimal Control
Christian Muench, Frans A. Oliehoek, Dariu M. Gavrila
Traffic scenarios are inherently interactive. Multiple decision-makers predict the actions of others and choose strategies that maximize their rewards. We view these interactions f…
CNN based Road User Detection using the 3D Radar Cube
Andras Palffy, Jiaao Dong, Julian F. P. Kooij +1
This letter presents a novel radar based, single-frame, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The…
Human Motion Trajectory Prediction: A Survey
Andrey Rudenko, Luigi Palmieri, Michael Herman +3
With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly…
Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery
Ries Uittenbogaard, Clint Sebastian, Julien Vijverberg +3
The current paradigm in privacy protection in street-view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurri…
The EuroCity Persons Dataset: A Novel Benchmark for Object Detection
Markus Braun, Sebastian Krebs, Fabian Flohr +1
Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection per…