Depth image-based plane detection

Abstract

Background

The emerging of depth-camera technology is paving the way for variety of new applications and it is believed that plane detection is one of them. In fact, planes are common in man-made living structures, thus their accurate detection can benefit many visual-based applications. The use of depth data allows detecting planes characterized by complicated pattern and texture, where texture-based plane detection algorithms usually fail. In this paper, we propose a robust Depth Image-based Plane Detection (DIPD) algorithm. The proposed approach starts from the highest planarity seed patch, and uses the estimated equation of the growing plane and a dynamic threshold function to steer the growing process. Aided with this mechanism, each seed patch can grow to its maximum extent, and then next seed patch starts to grow. This process is iteratively repeated so as to detect all the planes.

Results

Validated by extensive experiments on three datasets, the proposed DIPD algorithm can achieve 81% correct detection ratio which doubles the value compared with the state-of-the-art algorithms. Meanwhile, the runtime of the proposed algorithm is around 4 times of the fastest RANdom SAmple Consensus (RANSAC).

Conclusions

The proposed depth image-based plane detection algorithm can achieve state-of-the-art performance. In terms of applications, it could be used as the pre-processing step for planar object recognition, super-resolution of the intrinsically low resolution Time-of-Flight (ToF) depth images, and variety of other applications.