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We introduce a method for instance proposal generation for 3D point clouds.Existing techniques typically directly regress proposals in a singlefeed-forward step, leading to inaccurate estimation. We show that this servesas a critical bottleneck, and propose a method based on iterative bilateralfiltering with learned kernels. Following the spirit of bilateral filtering, weconsider both the deep feature embeddings of each point, as well as theirlocations in the 3D space. We show via synthetic experiments that our methodbrings drastic improvements when generating instance proposals for a givenpoint of interest. We further validate our method on the challenging ScanNetbenchmark, achieving the best instance segmentation performance amongst thesub-category of top-down methods.

NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds