# GeoNet: Deep Geodesic Networks for Point Cloud Analysis

## TL;DR

GeoNet is a novel deep learning architecture that models the intrinsic surface structure of point clouds using geodesic information, enhancing various 3D point cloud analysis tasks.

## Contribution

Introduces GeoNet, the first deep network to learn surface geodesic structures from point clouds, and demonstrates its effectiveness in multiple downstream tasks.

## Key findings

- Improves state-of-the-art in point upsampling
- Enhances normal estimation accuracy
- Boosts performance in mesh reconstruction and shape classification

## Abstract

Surface-based geodesic topology provides strong cues for object semantic analysis and geometric modeling. However, such connectivity information is lost in point clouds. Thus we introduce GeoNet, the first deep learning architecture trained to model the intrinsic structure of surfaces represented as point clouds. To demonstrate the applicability of learned geodesic-aware representations, we propose fusion schemes which use GeoNet in conjunction with other baseline or backbone networks, such as PU-Net and PointNet++, for down-stream point cloud analysis. Our method improves the state-of-the-art on multiple representative tasks that can benefit from understandings of the underlying surface topology, including point upsampling, normal estimation, mesh reconstruction and non-rigid shape classification.