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Found 5 papers, 2 papers with code
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
Moreover, we propose an adaptive weighted loss function group, specifically formulated to counteract the imbalance in the distribution of curb point clouds relative to other categories.
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.
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