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In order to develop and analyse the performance of large-scale colored point cloud upsampling, we built a large-scale colored point cloud dataset for training and evaluating the upsampling network. This large-scale colored point cloud dataset consists of 121 original colored point clouds, 43 of which were scanned by us, while the other 78 were obtained from the SIAT-PCQD, Moving Picture Experts Group (MPEG) point cloud, and Greyc 3D colored mesh database. These point clouds cover six categories, including animals, plants, toys, sculptures, people and others.
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RLED contains 80,400 images and corresponding events, we utilized a photometer to continuously measure scene illumination and calculate the illumination value after attenuation at the event camera. The capture scenes included city (35.0%), suburbs (10.3%), town (14.5%), village (17.8%), and valley (22.4%). Half of the RLED frames are captured at a frame rate of 25 fps, and the other half at 10 fps. The exposure time is set to 1ms, 3ms, and 5ms based on the varying environmental illumination.
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<div style="color: #d4d4d4; background-color: #1e1e1e; font-family: Consolas, 'Courier New', monospace; font-size: 18px; line-height: 24px; white-space: pre;">Weakly-supervised segmentation (WSS) has emerged as a solution to mitigate the conflict between annotation cost and model performance by adopting sparse annotation formats (e.g., point, scribble, block, etc.). Typical approaches attempt to exploit anatomy and topology priors to directly expand sparse annotations into pseudo-labels.
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