Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Gradient-Applied Weighted Loss for Details of 3D Shape in Single-View Reconstruction

Version 1 : Received: 6 November 2022 / Approved: 8 November 2022 / Online: 8 November 2022 (01:04:00 CET)

How to cite: Lee, J.; Kim, T.; Lee, G.; Choe, Y. Gradient-Applied Weighted Loss for Details of 3D Shape in Single-View Reconstruction. Preprints 2022, 2022110129. https://doi.org/10.20944/preprints202211.0129.v1 Lee, J.; Kim, T.; Lee, G.; Choe, Y. Gradient-Applied Weighted Loss for Details of 3D Shape in Single-View Reconstruction. Preprints 2022, 2022110129. https://doi.org/10.20944/preprints202211.0129.v1

Abstract

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

Keywords

3D shapes; single-view reconstruction; gradient map; fine-grained

Subject

Engineering, Electrical and Electronic Engineering

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