Improved Positional Encoding for Implicit Neural Representation based Compact Data Representation

Published: 01 Jan 2023, Last Modified: 25 Feb 2025CoRR 2023EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Positional encodings are employed to capture the high frequency information of the encoded signals in implicit neural representation (INR). In this paper, we propose a novel positional encoding method which improves the reconstruction quality of the INR. The proposed embedding method is more advantageous for the compact data representation because it has a greater number of frequency basis than the existing methods. Our experiments shows that the proposed method achieves significant gain in the rate-distortion performance without introducing any additional complexity in the compression task and higher reconstruction quality in novel view synthesis.
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