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Found 9 papers, 3 papers with code
MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception
Multi-sensor fusion models play a crucial role in autonomous driving perception, particularly in tasks like 3D object detection and HD map construction.
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation
To address this, we employ the Knowledge Distillation (KD) idea for efficient HD map construction for the first time and introduce a novel KD-based approach called MapDistill to transfer knowledge from a high-performance camera-LiDAR fusion model to a lightweight camera-only model.
Is Your HD Map Constructor Reliable under Sensor Corruptions?
These insights provide a pathway for developing more reliable HD map construction methods, which are essential for the advancement of autonomous driving technology.
Team Samsung-RAL: Technical Report for 2024 RoboDrive Challenge-Robust Map Segmentation Track
In this report, we describe the technical details of our submission to the 2024 RoboDrive Challenge Robust Map Segmentation Track.
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.
UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather
We devote UniMix to two main setups: 1) unsupervised domain adaption, adapting the model from the clear weather source domain to the adverse weather target domain; 2) domain generalization, learning a model that generalizes well to unseen scenes in adverse weather.
SimDistill: Simulated Multi-modal Distillation for BEV 3D Object Detection
Multi-view camera-based 3D object detection has become popular due to its low cost, but accurately inferring 3D geometry solely from camera data remains challenging and may lead to inferior performance.
On Robust Cross-View Consistency in Self-Supervised Monocular Depth Estimation
Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges.
JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes
A naive way is to accomplish them independently in a sequential or parallel manner, but there are many drawbacks, i. e., 1) the depth and VO results suffer from the inherent scale ambiguity issue; 2) the BEV layout is directly predicted from the front-view image without using any depth-related information, although the depth map contains useful geometry clues for inferring scene layouts.
