1.4k citations · 3.1k across the 72 of their papers we have counts for
18 papers · 1 filter
Simulating Road Spray Effects in Automotive Lidar Sensor Models
Clemens Linnhoff, Dominik Scheuble, Mario Bijelic +5
Modeling perception sensors is key for simulation based testing of automated driving functions. Beyond weather conditions themselves, sensors are also subjected to object dependent…
One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers
Rui Gong, Qin Wang, Dengxin Dai +1
Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. It can save the cos…
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
Lukas Hoyer, Dengxin Dai, Haoran Wang +1
In unsupervised domain adaptation (UDA), a model trained on source data (e.g. synthetic) is adapted to target data (e.g. real-world) without access to target annotation. Most previ…
Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts
Qi Fan, Mattia Segu, Yu-Wing Tai +4
Improving model's generalizability against domain shifts is crucial, especially for safety-critical applications such as autonomous driving. Real-world domain styles can vary subst…
MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction
Shaoshuai Shi, Li Jiang, Dengxin Dai +1
In this report, we present the 1st place solution for motion prediction track in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer framework for multimodal…
Motion Transformer with Global Intention Localization and Local Movement Refinement
Shaoshuai Shi, Li Jiang, Dengxin Dai +1
Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to make safe decisions. Existing works explore to directly predict future trajectori…
Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes
Yu-Huan Wu, Da Zhang, Le Zhang +4
Current efficient LiDAR-based detection frameworks are lacking in exploiting object relations, which naturally present in both spatial and temporal manners. To this end, we introdu…
HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection
Tim Broedermann, Christos Sakaridis, Dengxin Dai +1
Besides standard cameras, autonomous vehicles typically include multiple additional sensors, such as lidars and radars, which help acquire richer information for perceiving the con…
Bi-level Alignment for Cross-Domain Crowd Counting
Shenjian Gong, Shanshan Zhang, Jian Yang +2
Recently, crowd density estimation has received increasing attention. The main challenge for this task is to achieve high-quality manual annotations on a large amount of training d…
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…
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
Lukas Hoyer, Dengxin Dai, Luc Van Gool
Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source domain (e.g. synthetic data) to the target domain (e.g. real-world data) without requiring further…
Scribble-Supervised LiDAR Semantic Segmentation
Ozan Unal, Dengxin Dai, Luc Van Gool
Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data. While current literature focuses on fully-supervised…
Continual Test-Time Domain Adaptation
Qin Wang, Olga Fink, Luc Van Gool +1
Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target doma…
LiDAR Snowfall Simulation for Robust 3D Object Detection
Martin Hahner, Christos Sakaridis, Mario Bijelic +4
3D object detection is a central task for applications such as autonomous driving, in which the system needs to localize and classify surrounding traffic agents, even in the presen…
Pix2NeRF: Unsupervised Conditional -GAN for Single Image to Neural Radiance Fields Translation
Shengqu Cai, Anton Obukhov, Dengxin Dai +1
We propose a pipeline to generate Neural Radiance Fields~(NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as tra…
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…
Sound and Visual Representation Learning with Multiple Pretraining Tasks
Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool
Different self-supervised tasks (SSL) reveal different features from the data. The learned feature representations can exhibit different performance for each downstream task. In th…
End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization
Niclas Vödisch, Ozan Unal, Ke Li +2
Existing learning methods for LiDAR-based applications use 3D points scanned under a pre-determined beam configuration, e.g., the elevation angles of beams are often evenly distrib…