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The embeddings are computed using a lightweight neural\nnetwork operating on the points' local neighborhood. Finally, we formulate\npoint cloud oversegmentation as a graph partition problem with respect to the\nlearned embeddings.\n  This new approach allows us to set a new state-of-the-art in point cloud\noversegmentation by a significant margin, on a dense indoor dataset (S3DIS) and\na sparse outdoor one (vKITTI). Our best solution requires over five times fewer\nsuperpoints to reach similar performance than previously published methods on\nS3DIS. 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