An Improved Building Reconstruction Algorithm Based on Manhattan World Assumption and Line-Restricted Hypothetical Plane Fitting

An improved patch-based multiview stereo (PMVS) algorithm based on Manhattan world assumption and the line-restricted hypothetical plane fitting method according to buildings’ spatial characteristics is proposed. Different from the original PMVS algorithm, our approach generates seed points purely from 3D line segments instead of using those feature points. First, 3D line segments are extracted using the existing Line3D++ algorithm, and the 3D line segment clustering criterion of buildings is established based on Manhattan world assumption. Next, by using the normal direction obtained using the result of 3D line segment clustering, we propose a multihypothetical plane fitting algorithm based on the mean shift method. Then, through subdividing on the triangle mesh constructed based on the building hypothetical plane model, semidense point cloud can be quickly obtained, and it is used as seed points of the PMVS pipeline instead of the sparse and noisy seed points generated by PMVS itself. After that, dense point cloud can be obtained through the existing PMVS expansion pipeline. Finally, unit and integration experiments are designed; the test results show that the proposed algorithm is 15%∼23% faster than the original PMWS in running time, and at the same time, the reconstruction quality of buildings is improved as well by successfully removing many noise points in the buildings.

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Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Remote Sensing ◽

10.3390/rs12020320 ◽

2020 ◽

Vol 12 (2) ◽

pp. 320 ◽

Cited By ~ 1

Author(s):

Yaxin Li ◽

Wenbin Li ◽

Walid Darwish ◽

Shengjun Tang ◽

Yuling Hu ◽

...

Keyword(s):

Point Cloud ◽

Error Model ◽

Equal Weight ◽

Point Cloud Data ◽

Cloud Data ◽

Plane Fitting ◽

Fitting Method ◽

The Mean ◽

Rigorous Error ◽

Fitting Accuracy

Plane fitting is a fundamental operation for point cloud data processing. Most existing methods for point cloud plane fitting have been developed based on high-quality Lidar data giving equal weight to the point cloud data. In recent years, using low-quality RGB-Depth (RGB-D) sensors to generate 3D models has attracted much attention. However, with low-quality point cloud data, equal weight plane fitting methods are not optimal as the range errors of RGB-D sensors are distance-related. In this paper, we developed an accurate plane fitting method for a structured light (SL)-based RGB-D sensor. First, we derived an error model of a point cloud dataset from the SL-based RGB-D sensor through error propagation from the raw measurement to the point coordinates. A new cost function based on minimizing the radial distances with the derived rigorous error model was then proposed for the random sample consensus (RANSAC)-based plane fitting method. The experimental results demonstrated that our method is robust and practical for different operating ranges and different working conditions. In the experiments, for the operating ranges from 1.23 meters to 4.31 meters, the mean plane angle errors were about one degree, and the mean plane distance errors were less than six centimeters. When the dataset is of a large-depth-measurement scale, the proposed method can significantly improve the plane fitting accuracy, with a plane angle error of 0.5 degrees and a mean distance error of 4.7 cm, compared to 3.8 degrees and 16.8 cm, respectively, from the conventional un-weighted RANSAC method. The experimental results also demonstrate that the proposed method is applicable for different types of SL-based RGB-D sensor. The rigorous error model of the SL-based RGB-D sensor is essential for many applications such as in outlier detection and data authorization. Meanwhile, the precise plane fitting method developed in our research will benefit algorithms based on high-accuracy plane features such as depth calibration, 3D feature-based simultaneous localization and mapping (SLAM), and the generation of indoor building information models (BIMs).

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Less is more: Simple stimuli provoke more variable search strategies.

10.31234/osf.io/8kshm ◽

2020 ◽

Author(s):

Anna Nowakowska ◽

Alasdair D F Clarke ◽

Jessica Christie ◽

Josephine Reuther ◽

Amelia R. Hunt

Keyword(s):

Line Segment ◽

Search Strategies ◽

Complex Objects ◽

Search Behaviour ◽

Line Segments ◽

Surface Level ◽

Efficient Search ◽

Less Is More ◽

Dramatic Shift

We measured the efficiency of 30 participants as they searched through simple line segment stimuli and through a set of complex icons. We observed a dramatic shift from highly variable, and mostly inefficient, strategies with the line segments, to uniformly efficient search behaviour with the icons. These results demonstrate that changing what may initially appear to be irrelevant, surface-level details of the task can lead to large changes in measured behaviour, and that visual primitives are not always representative of more complex objects.

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Dense Point Cloud Mapping Based on RGB-D Camera in Dynamic Indoor Environment

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9327342 ◽

2020 ◽

Author(s):

Fangfang Zhang ◽

Qiyan Li ◽

Tingting Wang ◽

Yanhong Liu

Keyword(s):

Point Cloud ◽

Indoor Environment ◽

Dense Point

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Deep Compression for Dense Point Cloud Maps

IEEE Robotics and Automation Letters ◽

10.1109/lra.2021.3059633 ◽

2021 ◽

pp. 1-1

Author(s):

Louis Wiesmann ◽

Andres Milioto ◽

Xieyuanli Chen ◽

Cyrill Stachniss ◽

Jens Behley

Keyword(s):

Point Cloud ◽

Dense Point

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Accurate and dense point cloud generation for industrial Measurement via target-free photogrammetry

Optics and Lasers in Engineering ◽

10.1016/j.optlaseng.2020.106521 ◽

2021 ◽

Vol 140 ◽

pp. 106521

Author(s):

Nan Ye ◽

Hongyu Zhu ◽

Mingqiang Wei ◽

Liyan Zhang

Keyword(s):

Point Cloud ◽

Dense Point ◽

Industrial Measurement

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Explanation for Neon Color Effect in Achromatic Line Segments on Chromatic Inducers Based on the Multiple Interpretation Hypothesis

Perceptual and Motor Skills ◽

10.2466/pms.101.1.267-282 ◽

2005 ◽

Vol 101 (1) ◽

pp. 267-282

Author(s):

Seiyu Sohmiya

Keyword(s):

Visual System ◽

Line Segment ◽

Color Contrast ◽

Achromatic Color ◽

Line Segments ◽

Color Effect ◽

Complementary Color ◽

Multiple Interpretation ◽

Color Induction ◽

Simultaneous Color Contrast

In van Tuijl's neon configurations, an achromatic line segment on a blue inducer produces yellowish illusory color in the illusory area. This illusion has been explained based on the idea of the complementary color induced by the blue inducer. However, it is proposed here that this illusion can be also explained by introducing the assumption that the visual system unconsciously interprets an achromatic color as information that is constituted by transparent and nontransparent colors. If this explanation is correct, not only this illusion, but also the simultaneous color contrast illusion can be explained without using the idea of the complementary color induction.

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ALPHA-REGULAR STICK UNKNOTS

Journal of Knot Theory and Its Ramifications ◽

10.1142/s0218216512500599 ◽

2012 ◽

Vol 21 (06) ◽

pp. 1250059 ◽

Cited By ~ 1

Author(s):

CHRISTOPHER FRAYER ◽

CHRISTOPHER SCHAFHAUSER

Keyword(s):

Line Segment ◽

Line Segments

Suppose Pn is a regular n-gon in ℝ2. An embedding f : Pn ↪ ℝ3 is called an α-regular stick knot provided the image of each side of Pn under f is a line segment of length 1 and any two consecutive line segments meet at an angle of α. The main result of this paper proves the existence of α-regular stick unknots for odd n ≥ 7 with α in the range [Formula: see text]. All knots constructed will have trivial knot type, and we will show that any non-trivial α-regular stick knot must have [Formula: see text].

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