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Found 6 papers, 3 papers with code
CLRKDNet: Speeding up Lane Detection with Knowledge Distillation
Road lanes are integral components of the visual perception systems in intelligent vehicles, playing a pivotal role in safe navigation.
Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
This review looks back and analyzes the current state of achievements in the field of 3D lane detection research.
OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds
A Colored point cloud, as a simple and efficient 3D representation, has many advantages in various fields, including robotic navigation and scene reconstruction.
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation
Curb detection is a crucial function in intelligent driving, essential for determining drivable areas on the road.
Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training
Monocular 3D object detection plays a crucial role in autonomous driving.
Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation
we propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT semantic segmentation without access to the source (daytime) data during adaptation.
