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Found 7 papers, 0 papers with code
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation
Off-road semantic segmentation with fine-grained labels is necessary for autonomous vehicles to understand driving scenes, as the coarse-grained road detection can not satisfy off-road vehicles with various mechanical properties.
An Image-based Approach of Task-driven Driving Scene Categorization
A measure is learned to discriminate the scenes of different semantic attributes via contrastive learning, and a driving scene profiling and categorization method is developed based on that measure.
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning
With a set of human-annotated anchor patches, a feature representation is learned to discriminate regions with different traversability, a method of fine-grained semantic segmentation and mapping is subsequently developed for off-road scene understanding.
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study
Finally, a systematic survey to the existing efforts to solve the data hunger problem is conducted on both methodological and dataset's viewpoints, followed by an insightful discussion of remaining problems and open questions To the best of our knowledge, this is the first work to analyze the data hunger problem for 3D semantic segmentation using deep learning techniques that are addressed in the literature review, statistical analysis, and cross-dataset and cross-algorithm experiments.
Off-Road Drivable Area Extraction Using 3D LiDAR Data
We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application.
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
3D semantic segmentation is one of the key tasks for autonomous driving system.
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning
This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications.
Robotics
