Paper ID: 2311.06059

Improved Positional Encoding for Implicit Neural Representation based Compact Data Representation

Bharath Bhushan Damodaran, Francois Schnitzler, Anne Lambert, Pierre Hellier

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.

Submitted: Nov 10, 2023