What is it about?
This paper presents MilliNoise, a millimeter-wave sparse point cloud dataset captured in indoor scenarios. Each point in MilliNoise is accurately labelled as true/noise point by leveraging known information of the scenes and a motion capture system to obtain the ground truth position of the moving robot. MilliNoise has been post-processed as well to allow moving the denoising task into the regression framework, by labelling each point with the distance to its closest obstacle in the scene.
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Photo by Uriel SC on Unsplash
Why is it important?
It's the first mmWave point cloud dataset with each point labelled with a sub-millimeter accuracy for indoor scenarios.
Perspectives
You can use it to test classification and regression tools for detecting noise points and object points, for testing reconstruction algorithms (SLAM), object tracking...
Walter Brescia
Politecnico di Bari
Read the Original
This page is a summary of: MilliNoise, April 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3625468.3652189.
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