Decodable and Sample Invariant Continuous Object Encoder

Dehao Yuan, Furong Huang, Cornelia Fermuller, Yiannis Aloimonos

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Keywords: Point Cloud, PointNet, Function Mapping, Encoder, Noise Robustness

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TL;DR: We propose a function encoder that converts its sample into a fixed-length vector. The encoding is robust to distribution and density variation of the sampling.

Abstract:

We propose Hyper-Dimensional Function Encoding (HDFE). Given samples of a continuous object (e.g. a function), HDFE produces an explicit vector representation of the given object, invariant to the sample distribution and density. Sample distribution and density invariance enables HDFE to consistently encode continuous objects regardless of their sampling, and therefore allows neural networks to receive continuous objects as inputs for machine learning tasks, such as classification and regression. Besides, HDFE does not require any training and is proved to map the object into an organized embedding space, which facilitates the training of the downstream tasks. In addition, the encoding is decodable, which enables neural networks to regress continuous objects
by regressing their encodings. Therefore, HDFE serves as an interface for processing continuous objects.

We apply HDFE to function-to-function mapping, where vanilla HDFE achieves competitive performance with the state-of-the-art algorithm. We apply HDFE to point cloud surface normal estimation, where a simple replacement from PointNet to HDFE leads to 12\% and 15\% error reductions in two benchmarks.
In addition, by integrating HDFE into the PointNet-based SOTA network, we improve the SOTA baseline by 2.5\% and 1.7\% on the same benchmarks.

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Primary Area: representation learning for computer vision, audio, language, and other modalities

Submission Number: 1026