…In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. We map all moving-x classes of the original SemanticKITTI semantic segmentation benchmark to a single moving object class. Citation Citation. More information on the task and the metric, you can find in our publication related to the task: @article{chen2021ral, title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach
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The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent
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…It can be applied in multiple tasks, such as object detection, instance segmentation, semantic segmentation, free-space segmentation, and waterline segmentation.
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Precise segmentation of architectural structures provides detailed information about various building components, enhancing our understanding and interaction with our built environment. To overcome this shortfall, this paper introduces a semantically-enriched, photo-realistic 3D architectural models dataset and benchmark for semantic segmentation.
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…By collecting data in simulations, multi-modal sensor data and precise ground truth labels are obtainable such as the RGB image, depth image, semantic segmentation, change segmentation, camera poses, and
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…Leaf wood labels were transferred from contemporaneous (2021) TLS acquisition, for which segmentation was done using LeWoS and onscreen post correction.
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🤖 Robo3D - The SemanticKITTI-C Benchmark SemanticKITTI-C is an evaluation benchmark heading toward robust and reliable 3D semantic segmentation in autonomous driving.
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