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Found 9 papers, 1 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.
End-to-end Keyword Spotting using Neural Architecture Search and Quantization
This paper introduces neural architecture search (NAS) for the automatic discovery of end-to-end keyword spotting (KWS) models in limited resource environments.
Ranked #15 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V2 12 metric)
Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and Quantization
This paper introduces neural architecture search (NAS) for the automatic discovery of small models for keyword spotting (KWS) in limited resource environments.
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.
Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation
The effectiveness of the proposed network architecture as well as the automated data generation process is demonstrated on a manually annotated ground truth dataset.
