Robustness-aware 3d object detection in autonomous driving: A review and outlook
In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …
accurately assessing the state of the surrounding environment, thereby enabling informed …
Multi-modal data-efficient 3d scene understanding for autonomous driving
Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …
The robodrive challenge: Drive anytime anywhere in any condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is
paramount for the safe deployment of vehicles. Challenges such as adverse weather …
paramount for the safe deployment of vehicles. Challenges such as adverse weather …
The robodepth challenge: Methods and advancements towards robust depth estimation
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse
weather conditions, sensor failure, and noise contamination, is desirable for safety-critical …
weather conditions, sensor failure, and noise contamination, is desirable for safety-critical …
Multi-Space Alignments Towards Universal LiDAR Segmentation
A unified and versatile LiDAR segmentation model with strong robustness and
generalizability is desirable for safe autonomous driving perception. This work presents …
generalizability is desirable for safe autonomous driving perception. This work presents …
Uniworld: Autonomous driving pre-training via world models
In this paper, we draw inspiration from Alberto Elfes' pioneering work in 1989, where he
introduced the concept of the occupancy grid as World Models for robots. We imbue the …
introduced the concept of the occupancy grid as World Models for robots. We imbue the …
A survey on occupancy perception for autonomous driving: The information fusion perspective
Abstract 3D occupancy perception technology aims to observe and understand dense 3D
environments for autonomous vehicles. Owing to its comprehensive perception capability …
environments for autonomous vehicles. Owing to its comprehensive perception capability …
Calib3d: Calibrating model preferences for reliable 3d scene understanding
Safety-critical 3D scene understanding tasks necessitate not only accurate but also
confident predictions from 3D perception models. This study introduces Calib3D, a …
confident predictions from 3D perception models. This study introduces Calib3D, a …
OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks
S Sirko-Galouchenko, A Boulch… - Proceedings of the …, 2024 - openaccess.thecvf.com
We introduce a self-supervised pretraining method called OccFeat for camera-only Bird's-
Eye-View (BEV) segmentation networks. With OccFeat we pretrain a BEV network via …
Eye-View (BEV) segmentation networks. With OccFeat we pretrain a BEV network via …
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is
crucial for understanding complex 3D environments. Traditional approaches often rely on …
crucial for understanding complex 3D environments. Traditional approaches often rely on …