PaintNet: 3D Learning of Pose Paths Generators for Robotic Spray Painting
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Optimization and planning methods for tasks involving 3D objects often rely on prior knowledge and ad-hoc heuristics. In this work, we target learning-based long-horizon path generation by leveraging recent advances in 3D deep learning. We present PaintNet, the first dataset for learning robotic spray painting of free-form 3D objects. PaintNet includes more than 800 object meshes and the associated painting strokes collected in a real industrial setting. We then introduce a novel 3D deep learning method to tackle this task and operate on unstructured input spaces – point clouds – and mix-structured output spaces – unordered sets of painting strokes. Our extensive experimental analysis demonstrates the capabilities of our method to predict smooth output strokes that cover up to 95 to ground-truth paint coverage. The PaintNet dataset and an implementation of our proposed approach will be released at https://gabrieletiboni.github.io/paintnet.
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