3D planar patch extraction from stereo using probabilistic region growing

TL;DR

This paper introduces a probabilistic region growing algorithm for extracting 3D planar patches from stereo images, leveraging initial segmentation and prior noise models for robust scene understanding.

Contribution

It presents a novel multi-seed, probabilistic 3D planar patch extraction method that integrates intensity-based segmentation and prior noise information for improved accuracy.

Findings

Effective on real and synthetic datasets

Handles regular and non-regular sampling

Suitable for robot navigation tasks

Abstract

This article presents a novel 3D planar patch extraction method using a probabilistic region growing algorithm. Our method works by simultaneously initiating multiple planar patches from seed points, the latter determined by an intensity-based 2D segmentation algorithm in the stereo-pair images. The patches are grown incrementally and in parallel as 3D scene points are considered for membership, using a probabilistic distance likelihood measure. In addition, we have incorporated prior information based on the noise model in the 2D images and the scene configuration but also include the intensity information resulting from the initial segmentation. This method works well across many different data-sets, involving real and synthetic examples of both regularly and non-regularly sampled data, and is fast enough that may be used for robot navigation tasks of path detection and obstacle avoidance.