EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera

TL;DR

EGP3D introduces an edge-guided, geometry-preserving super-resolution method for RGB-D point clouds, effectively enhancing edge details and geometric accuracy in real-world noisy environments.

Contribution

The paper proposes a novel edge-guided super-resolution approach with a multi-faceted loss function and a new real-world dataset for RGB-D point cloud enhancement.

Findings

Superior edge preservation in super-resolved point clouds

Effective handling of real-world noise and stray-light effects

Outperforms existing methods in geometric detail retention

Abstract

Point clouds or depth images captured by current RGB-D cameras often suffer from low resolution, rendering them insufficient for applications such as 3D reconstruction and robots. Existing point cloud super-resolution (PCSR) methods are either constrained by geometric artifacts or lack attention to edge details. To address these issues, we propose an edge-guided geometric-preserving 3D point cloud super-resolution (EGP3D) method tailored for RGB-D cameras. Our approach innovatively optimizes the point cloud with an edge constraint on a projected 2D space, thereby ensuring high-quality edge preservation in the 3D PCSR task. To tackle geometric optimization challenges in super-resolution point clouds, particularly preserving edge shapes and smoothness, we introduce a multi-faceted loss function that simultaneously optimizes the Chamfer distance, Hausdorff distance, and gradient smoothness. Existing datasets used for point cloud upsampling are predominantly synthetic and inadequately represent real-world scenarios, neglecting noise and stray light effects. To address the scarcity of realistic RGB-D data for PCSR tasks, we built a dataset that captures real-world noise and stray-light effects, offering a more accurate representation of authentic environments. Validated through simulations and real-world experiments, the proposed method exhibited superior performance in preserving edge clarity and geometric details.