Proceedings ArticleDOI
Split and Merge for Accurate Plane Segmentation in RGB-D Images
Yigong Zhang,Tao Lu,Jian Yang,Hui Kong +3 more
- pp 49-54
Reads0
Chats0
TLDR
An accurate and efficient method to detect planar surfaces indoors based on an RGB-D camera using a graph-based segmentation approach and can detect planes indoors at a frame rate of 10Hz, and can achieve very promising performance.Abstract:
In this paper, we propose an accurate and efficient method to detect planar surfaces indoors based on an RGB-D camera. First, we segment the RGB image using a graph-based segmentation approach because of its efficiency and capability in preserving sharp region borders. The graph-based color segmentation methods usually result in over-segmentation or under-segmentation. Then to achieve better plane segmentation results, we propose a split-andmerge strategy. We first segment the planes in the split step by applying a random sampling and consensus (RANSAC) approach to each graph-derived point cloud based on a plane-fitting mean squared error (MSE). In the merge step, we can simultaneously merge some over-segmented regions obtained from the split step by a maximal clique clustering approach. Experiment demonstrates that our plane segmentation algorithm can detect planes indoors at a frame rate of 10Hz, and can achieve very promising performance.read more
Citations
More filters
Journal ArticleDOI
Advanced Methods for Point Cloud Processing and Simplification
TL;DR: The approach to preprocess an input point cloud is introduced in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features by applying advanced image processing methods in combination with the quantization of physical space points.
Proceedings ArticleDOI
Accurate Camera Syncronization Using Deep-Shallow Mixed Models
TL;DR: This work proposes a method for synchronizing nearly all kinds of cameras given that they are connected into on PC and can achieve the mean absolute difference (MSD) below 12 ms, which is totally meet the requirement of stereo vision or other multiple vision tasks.
Proceedings ArticleDOI
Dealing with the structured scene in visual odometry(VO): Incomplete SURF
TL;DR: This paper tried the proposed combined SURF and Incomplete SURF method in the authors' self-established dataset, and succeeded in estimating the transition matrix where pure SURF failed.
Proceedings ArticleDOI
Real Time and Robust 6D Pose Estimation of RGBD Data for Robotic Bin Picking
TL;DR: This paper proposes a real time and robust method to address the issues of 6D pose estimation for robot bin picking utilizing a low cost 3D sensor that uses a pinhole camera model and the geometric relationship to correlate the point cloud data and RGB pixels, which is faster and almost same accurate compared with registration based methods.
References
More filters
Journal ArticleDOI
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
Journal ArticleDOI
Efficient Graph-Based Image Segmentation
TL;DR: An efficient segmentation algorithm is developed based on a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image and it is shown that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties.
Proceedings ArticleDOI
3D is here: Point Cloud Library (PCL)
Radu Bogdan Rusu,Steve Cousins +1 more
TL;DR: PCL (Point Cloud Library) is presented, an advanced and extensive approach to the subject of 3D perception that contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.
Proceedings ArticleDOI
SUN RGB-D: A RGB-D scene understanding benchmark suite
TL;DR: This paper introduces an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks, and presents a dataset that enables the train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.
Proceedings ArticleDOI
Dense visual SLAM for RGB-D cameras
TL;DR: This paper proposes a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels, and proposes an entropy-based similarity measure for keyframe selection and loop closure detection.