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- W2210040239 abstract "We present a novel, fast, and compact method to improve semantic segmentation of three-dimensional (3-D) point clouds, which is able to learn and exploit common contextual relations between observed structures and objects. Introducing 3-D Entangled Forests (3-DEF), we extend the concept of entangled features for decision trees to 3-D point clouds, enabling the classifier not only to learn, which labels are likely to occur close to each other, but also in which specific geometric configuration. Operating on a plane-based representation of a point cloud, our method does not require a final smoothing step and achieves state-of-the-art results on the NYU Depth Dataset in a single inference step. This compactness in turn allows for fast processing times, a crucial factor to consider for online applications on robotic platforms. In a thorough evaluation, we demonstrate the expressiveness of our new 3-D entangled feature set and the importance of spatial context in the scope of semantic segmentation." @default.
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- W2210040239 date "2016-01-01" @default.
- W2210040239 modified "2023-10-18" @default.
- W2210040239 title "Enhancing Semantic Segmentation for Robotics: The Power of 3-D Entangled Forests" @default.
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- W2210040239 doi "https://doi.org/10.1109/lra.2015.2506118" @default.
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