986 citations · 1.1k across the 11 of their papers we have counts for
11 papers
Neural Rendering for Sensor Adaptation in 3D Object Detection
Felix Embacher, David Holtz, Jonas Uhrig +2
Autonomous vehicles often have varying camera sensor setups, which is inevitable due to restricted placement options for different vehicle types. Training a perception model on one…
DualAD: Disentangling the Dynamic and Static World for End-to-End Driving
Simon Doll, Niklas Hanselmann, Lukas Schneider +4
State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by p…
ADA-Track++: End-to-End Multi-Camera 3D Multi-Object Tracking with Alternating Detection and Association
Shuxiao Ding, Lukas Schneider, Marius Cordts +1
Many query-based approaches for 3D Multi-Object Tracking (MOT) adopt the tracking-by-attention paradigm, utilizing track queries for identity-consistent detection and object querie…
3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking
Shuxiao Ding, Eike Rehder, Lukas Schneider +2
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning. Based…
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View
Peizheng Li, Shuxiao Ding, Xieyuanli Chen +3
Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic. While bird's-ey…
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…
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
Nils Gählert, Nicolas Jourdan, Marius Cordts +2
Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detec…
The Stixel world: A medium-level representation of traffic scenes
Marius Cordts, Timo Rehfeld, Lukas Schneider +5
Recent progress in advanced driver assistance systems and the race towards autonomous vehicles is mainly driven by two factors: (1) increasingly sophisticated algorithms that inter…
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos +6
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, esp…
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
Jonas Uhrig, Marius Cordts, Uwe Franke +1
Recent approaches for instance-aware semantic labeling have augmented convolutional neural networks (CNNs) with complex multi-task architectures or computationally expensive graphi…
A Discontinuous Neural Network for Non-Negative Sparse Approximation
Martijn Arts, Marius Cordts, Monika Gorin +2
This paper investigates a discontinuous neural network which is used as a model of the mammalian olfactory system and can more generally be applied to solve non-negative sparse app…