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Found 5 papers, 0 papers with code
A Realism Metric for Generated LiDAR Point Clouds
In a series of experiments, we demonstrate the application of our metric to determine the realism of generated LiDAR data and compare the realism estimation of our metric to the performance of a segmentation model.
A Survey on Deep Domain Adaptation for LiDAR Perception
Scalable systems for automated driving have to reliably cope with an open-world setting.
Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data
We show that this continuous representation is suitable to encode geometric and semantic properties of extensive outdoor scenes without the need for spatial discretization (thus avoiding the trade-off between level of scene detail and the scene extent that can be covered).
Ranked #5 on
3D Semantic Scene Completion
on SemanticKITTI
Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study
Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment.
CNN-based synthesis of realistic high-resolution LiDAR data
This paper presents a novel CNN-based approach for synthesizing high-resolution LiDAR point cloud data.
