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title
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2026-02-23 21:53:57
raw text
Self-Supervised Point Cloud Completion via Inpainting Self-Supervised Point Cloud Completion via Inpainting Himangi Mittal Brian Okorn Arpit Jangid David Held Robotics Institute Carnegie Mellon University [Paper] [Arxiv Paper] [Video] We adopt an inpainting-based approach for self-supervised point cloud completion to train our network using only partial point clouds. Given a partial point cloud as input, we randomly remove regions from it and train the network to complete these regions using the input as the pseudo-ground truth. The loss is only applied to the regions which have points in the observed input partial point cloud (red) . Since, the network cannot differentiate between synthetic and natural occlusions, the network predicts a complete point cloud. Download Paper Abstract When navigating in urban environments, many of the objects that need to be tracked and avoided are heavily occluded. Planning and tracking using these...
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