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Found 7 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.
Point Cloud Generation with Continuous Conditioning
Generative models can be used to synthesize 3D objects of high quality and diversity.
Semi-Local Convolutions for LiDAR Scan Processing
A number of applications, such as mobile robots or automated vehicles, use LiDAR sensors to obtain detailed information about their three-dimensional surroundings.
Quantifying point cloud realism through adversarially learned latent representations
In a series of experiments, we confirm the soundness of our metric by applying it in controllable task setups and on unseen data.
A Survey on Deep Domain Adaptation for LiDAR Perception
Scalable systems for automated driving have to reliably cope with an open-world setting.
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.
