LiDAR object detection algorithms based on neural networks for autonomous driving require
large amounts of data for training, validation, and testing. As real-world data collection and …

Point cloud data, representing the precise 3D layout of the scene, quickly drives the
research of 3D object detection. However, the challenge arises due to the rapid iteration of …

We consider the problem of cross-sensor domain adaptation in the context of LiDAR-based
3D object detection and propose Stationary Object Aggregation Pseudo-labelling (SOAP) to …

Learning models on one labeled dataset that generalize well on another domain is a difficult
task, as several shifts might happen between the data domains. This is notably the case for …

Every autonomous driving dataset has a different configuration of sensors, originating from
distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to …

Deploying 3D detectors in unfamiliar domains has been demonstrated to result in a
significant 70-90% drop in detection rate due to variations in lidar, geography, or weather …

We introduce Multi-Source 3D (MS3D), a new self-training pipeline for unsupervised domain
adaptation in 3D object detection. Despite the remarkable accuracy of 3D detectors, they …

Perception algorithms for autonomous vehicles demand large, labeled datasets. Real-world
data acquisition and annotation costs are high, making synthetic data from simulation a cost …

Depth estimation is one of the essential tasks to be addressed when creating mobile
autonomous systems. While monocular depth estimation methods have improved in recent …

Learning models on one labeled dataset that generalize well on another domain is a difficult
task, as several shifts might happen between the data domains. This is notably the case for …