Efficient Hierarchical Graph-Based Segmentation of RGBD Videos

TL;DR

This paper introduces a scalable, hierarchical graph-based algorithm for real-time segmentation of RGBD videos, effectively combining depth, color, and temporal data for robust scene understanding.

Contribution

It proposes a novel multistage, hierarchical approach that processes RGBD videos incrementally, improving segmentation quality and efficiency over existing methods.

Findings

Effective segmentation of challenging RGBD sequences

Incremental processing enables real-time video segmentation

Hierarchical approach improves segmentation accuracy

Abstract

We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach. Our algorithm processes a moving window over several point clouds to group similar regions over a graph, resulting in an initial over-segmentation. These regions are then merged to yield a dendrogram using agglomerative clustering via a minimum spanning tree algorithm. Bipartite graph matching at a given level of the hierarchical tree yields the final segmentation of the point clouds by maintaining region identities over arbitrarily long periods of time. We show that a multistage segmentation with depth then color yields better results than a linear combination of depth and color. Due to its incremental processing, our algorithm can process videos of any length and in a streaming pipeline. The algorithm's ability to produce robust, efficient segmentation is demonstrated with numerous experimental results on challenging sequences from our own as well as public RGBD data sets.