549 citations · 1.5k across the 32 of their papers we have counts for
21 papers · 1 filter
Vernata: Self-Supervised Learning of LiDAR Point Representations
Oliver Lemke, Alexander Liniger, Abel Gawel +1
LiDAR serves as a primary sensing modality for robots operating in outdoor environments. However, the performance of deep learning models in this domain is severely limited by the…
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…
U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization
Andrea Boscolo Camiletto, Alfredo Bochicchio, Alexander Liniger +2
Efficient relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails. Recent advances in Bird's-Eye-View (BEV) segme…
Prior Based Online Lane Graph Extraction from Single Onboard Camera Image
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
The local road network information is essential for autonomous navigation. This information is commonly obtained from offline HD-Maps in terms of lane graphs. However, the local ro…
Improving Online Lane Graph Extraction by Object-Lane Clustering
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
Autonomous driving requires accurate local scene understanding information. To this end, autonomous agents deploy object detection and online BEV lane graph extraction methods as a…
Online Lane Graph Extraction from Onboard Video
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
Autonomous driving requires a structured understanding of the surrounding road network to navigate. One of the most common and useful representation of such an understanding is don…
Piecewise Planar Hulls for Semi-Supervised Learning of 3D Shape and Pose from 2D Images
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
We study the problem of estimating 3D shape and pose of an object in terms of keypoints, from a single 2D image. The shape and pose are learned directly from images collected by ca…
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields
Soomin Lee, Le Chen, Jiahao Wang +3
In this paper, we tackle the problem of active robotic 3D reconstruction of an object. In particular, we study how a mobile robot with an arm-held camera can select a favorable num…
Deep Gradient Learning for Efficient Camouflaged Object Detection
Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou +3
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branc…
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
Vaishakh Patil, Christos Sakaridis, Alexander Liniger +1
Monocular depth estimation is vital for scene understanding and downstream tasks. We focus on the supervised setup, in which ground-truth depth is available only at training time.…
Adiabatic Quantum Computing for Multi Object Tracking
Jan-Nico Zaech, Alexander Liniger, Martin Danelljan +2
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. The association step naturally lead…
End-to-End Learning of Multi-category 3D Pose and Shape Estimation
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
In this paper, we study the representation of the shape and pose of objects using their keypoints. Therefore, we propose an end-to-end method that simultaneously detects 2D keypoin…
Topology Preserving Local Road Network Estimation from Single Onboard Camera Image
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
Knowledge of the road network topology is crucial for autonomous planning and navigation. Yet, recovering such topology from a single image has only been explored in part. Furtherm…
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images
Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1
Autonomous navigation requires structured representation of the road network and instance-wise identification of the other traffic agents. Since the traffic scene is defined on the…
End-to-End Urban Driving by Imitating a Reinforcement Learning Coach
Zhejun Zhang, Alexander Liniger, Dengxin Dai +2
End-to-end approaches to autonomous driving commonly rely on expert demonstrations. Although humans are good drivers, they are not good coaches for end-to-end algorithms that deman…
Learnable Online Graph Representations for 3D Multi-Object Tracking
Jan-Nico Zaech, Dengxin Dai, Alexander Liniger +2
Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality. Most re…
Understanding Bird's-Eye View of Road Semantics using an Onboard Camera
Yigit Baran Can, Alexander Liniger, Ozan Unal +2
Autonomous navigation requires scene understanding of the action-space to move or anticipate events. For planner agents moving on the ground plane, such as autonomous vehicles, thi…
Learning Accurate and Human-Like Driving using Semantic Maps and Attention
Simon Hecker, Dengxin Dai, Alexander Liniger +1
This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. To tackle the first issue we exploit semantic and visual maps from HE…
Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context
Jan-Nico Zaech, Dengxin Dai, Alexander Liniger +1
This work studies the problem of predicting the sequence of future actions for surround vehicles in real-world driving scenarios. To this aim, we make three main contributions. The…
Quantifying Data Augmentation for LiDAR based 3D Object Detection
Martin Hahner, Dengxin Dai, Alexander Liniger +1
In this work, we shed light on different data augmentation techniques commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection. For the bulk of our experiment…
Learning a Curve Guardian for Motorcycles
Simon Hecker, Alexander Liniger, Henrik Maurenbrecher +2
Up to 17% of all motorcycle accidents occur when the rider is maneuvering through a curve and the main cause of curve accidents can be attributed to inappropriate speed and wrong i…