Fast Graph-Based Object Segmentation for RGB-D Images

TL;DR

This paper introduces a fast, graph-based method for segmenting objects in RGB-D images, crucial for robotic grasping, that does not depend on machine learning and effectively handles various textures and missing depth data.

Contribution

The paper presents a novel, feature-free, graph-based segmentation algorithm utilizing depth-enhanced edge detection and simple cost functions, achieving efficient partitioning with O(NlogN) complexity.

Findings

Effective segmentation across diverse textures

Robust performance with missing depth data

Faster than comparable methods

Abstract

Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graph- based approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine learning. We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues. The cost functions are used to build an undirected graph, which is partitioned using the concept of internal and external differences between graph regions. The partitioning is fast with O(NlogN) complexity. We also discuss ways to deal with missing depth information. We test the approach on different publicly available RGB-D object datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset, and compare the results with other existing methods.