Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds

Tian, Yifei; Song, Wei; Chen, Long; Sung, Yunsick; Kwak, Jeonghoon; Sun, Shuai

doi:10.3390/app10051744

Cited by 16 publications

(11 citation statements)

References 26 publications

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“…Hough Transform is well known as being capable of detecting particular shapes in images, such as circles and lines, in 2D images by voting in parameter space [17]. It is transferred to three dimensions, by adapting parameter space in a polar coordination system for plane extraction [11].…”

Section: Related Workmentioning

confidence: 99%

“…In region-growing approaches, the plane model is initialized with the first seed, thus the selection of seeds is crucial in the region-growing algorithm. The DPD (Depth-driven Plane Detection) approach [10] selects seed patches densely throughout the whole image, with higher planarity first [11]. RANSAC-based methods [8,9] selects seeds randomly from the whole sample set.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Plane Extraction Approach in Inverse Depth Images Based on Region-Growing

Han

Wang

Leng

et al. 2021

Sensors

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Planar surfaces are prevalent components of man-made indoor scenes, and plane extraction plays a vital role in practical applications of computer vision and robotics, such as scene understanding, and mobile manipulation. Nowadays, most plane extraction methods are based on reconstruction of the scene. In this paper, plane representation is formulated in inverse-depth images. Based on this representation, we explored the potential to extract planes in images directly. A fast plane extraction approach, which employs the region growing algorithm in inverse-depth images, is presented. This approach consists of two main components: seeding, and region growing. In the seeding component, seeds are carefully selected locally in grid cells to improve exploration efficiency. After seeding, each seed begins to grow into a continuous plane in succession. Both greedy policy and a normal coherence check are employed to find boundaries accurately. During growth, neighbor coplanar planes are checked and merged to overcome the over-segmentation problem. Through experiments on public datasets and generated saw-tooth images, the proposed approach achieves 80.2% CDR (Correct Detection Rate) on the ABW SegComp Dataset, which has proven that it has comparable performance with the state-of-the-art. The proposed approach runs at 5 Hz on typical 680 × 480 images, which has shown its potential in real-time practical applications in computer vision and robotics with further improvement.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Plane Extraction Approach in Inverse Depth Images Based on Region-Growing

Han

Wang

Leng

et al. 2021

Sensors

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“…Geometry model based plane estimation algorithms have been extensively studied for many years. Some approaches use Random Sample Consensus (RANSAC) [11] or Hough Transformation [32] achieving solid results and near realtime running speed. However, such techniques consume large computational resources when dealing with dense inputs (point cloud or depth image), and require additional sensors for depth estimation.…”

Section: Introductionmentioning

confidence: 99%

PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN

Xie

Rambach

Shu

et al. 2021

Preprint

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Instance segmentation of planar regions in indoor scenes benefits visual SLAM and other applications such as augmented reality (AR) where scene understanding is required. Existing methods built upon two-stage frameworks show satisfactory accuracy but are limited by low frame rates. In this work, we propose a real-time deep neural architecture that estimates piece-wise planar regions from a single RGB image. Our model employs a variant of a fast single-stage CNN architecture to segment plane instances. Considering the particularity of the target detected, we propose Fast Feature Nonmaximum Suppression (FF-NMS) to reduce the suppression errors resulted from overlapping bounding boxes of planes. We also utilize a Residual Feature Augmentation module in the Feature Pyramid Network (FPN) . Our method achieves significantly higher frame-rates and comparable segmentation accuracy against two-stage methods. We automatically label over 70,000 images as ground truth from the Stanford 2D-3D-Semantics dataset. Moreover, we incorporate our method with a state-of-the-art planar SLAM and validate its benefits.

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“…The virtual environment can be reconstructed to provide situations that can occur in the real environment using the 3D point cloud measured by light detection and range (LiDAR) [5][6][7][8]. The movement of virtual objects, such as a person, is expressed by using divided moving objects based on the measured 3D point cloud [9][10][11]. In order to control these objects, a pose predicted as a segmented 3D point cloud is used [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

DeepLabV3-Refiner-Based Semantic Segmentation Model for Dense 3D Point Clouds

Kwak

Sung

2021

Remote Sensing

Self Cite

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Three-dimensional virtual environments can be configured as test environments of autonomous things, and remote sensing by 3D point clouds collected by light detection and range (LiDAR) can be used to detect virtual human objects by segmenting collected 3D point clouds in a virtual environment. The use of a traditional encoder-decoder model, such as DeepLabV3, improves the quality of the low-density 3D point clouds of human objects, where the quality is determined by the measurement gap of the LiDAR lasers. However, whenever a human object with a surrounding environment in a 3D point cloud is used by the traditional encoder-decoder model, it is difficult to increase the density fitting of the human object. This paper proposes a DeepLabV3-Refiner model, which is a model that refines the fit of human objects using human objects whose density has been increased through DeepLabV3. An RGB image that has a segmented human object is defined as a dense segmented image. DeepLabV3 is used to make predictions of dense segmented images and 3D point clouds for human objects in 3D point clouds. In the Refiner model, the results of DeepLabV3 are refined to fit human objects, and a dense segmented image fit to human objects is predicted. The dense 3D point cloud is calculated using the dense segmented image provided by the DeepLabV3-Refiner model. The 3D point clouds that were analyzed by the DeepLabV3-Refiner model had a 4-fold increase in density, which was verified experimentally. The proposed method had a 0.6% increase in density accuracy compared to that of DeepLabV3, and a 2.8-fold increase in the density corresponding to the human object. The proposed method was able to provide a 3D point cloud that increased the density to fit the human object. The proposed method can be used to provide an accurate 3D virtual environment by using the improved 3D point clouds.

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Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds

Cited by 16 publications

References 26 publications

A Plane Extraction Approach in Inverse Depth Images Based on Region-Growing

A Plane Extraction Approach in Inverse Depth Images Based on Region-Growing

PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN

DeepLabV3-Refiner-Based Semantic Segmentation Model for Dense 3D Point Clouds

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