---
title: "Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields"
authors:
  - "Peng-Shuai Wang"
  - "Yang Liu"
  - "Yu-Qi Yang"
  - "Xin Tong"
doi: "10.24963/ijcai.2021/151"
url: "https://ijcai.org/proceedings/2021/151"
pdf: "https://ijcai.org/proceedings/2021/0151.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "1091-1097"
track: "Main Track"
keywords:
  - "Computer Vision: 2D and 3D Computer Vision"
---

# Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields

Peng-Shuai Wang, Yang Liu, Yu-Qi Yang, Xin Tong

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 1091-1097. DOI: [10.24963/ijcai.2021/151](https://doi.org/10.24963/ijcai.2021/151)

## Abstract

Multilayer perceptrons (MLPs) have been successfully used to represent 3D
shapes implicitly and compactly, by mapping 3D coordinates to the
corresponding signed distance values or occupancy values. In this paper, we
propose a novel positional encoding scheme, called Spline Positional
Encoding, to map the input coordinates to a high dimensional space before
passing them to MLPs, which help recover 3D signed distance fields with
fine-scale geometric details from unorganized 3D point clouds.  We verified
the superiority of our approach over other positional encoding schemes on
tasks of 3D shape reconstruction and 3D shape space learning from input
point clouds. The efficacy of our approach extended to image reconstruction
is also demonstrated and evaluated.