- TL;DR: We present and evaluate sampling-based point cloud decoders that outperform the baseline MLP approach by better matching the semantics of point clouds.
- Abstract: Point clouds are a flexible and ubiquitous way to represent 3D objects with arbitrary resolution and precision. Previous work has shown that adapting encoder networks to match the semantics of their input point clouds can significantly improve their effectiveness over naive feedforward alternatives. However, the vast majority of work on point-cloud decoders are still based on fully-connected networks that map shape representations to a fixed number of output points. In this work, we investigate decoder architectures that more closely match the semantics of variable sized point clouds. Specifically, we study sample-based point-cloud decoders that map a shape representation to a point feature distribution, allowing an arbitrary number of sampled features to be transformed into individual output points. We develop three sample-based decoder architectures and compare their performance to each other and show their improved effectiveness over feedforward architectures. In addition, we investigate the learned distributions to gain insight into the output transformation. Our work is available as an extensible software platform to reproduce these results and serve as a baseline for future work.
- Keywords: point cloud, autoencoder
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