Automatic upright orientation and good view recognition for 3D man-made models

Lin, Cong-Kai; Tai, Wen‐Kai

doi:10.1016/j.patcog.2011.10.022

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…We use Eq. 16 and Eq.18 to analyze the errors of the rotation angles along the three coordinate axes one by one to evaluate the performance of our method along each coordinate axis. Fig.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

“…This type of method usually simplifies the surface structure of the 3D object, selects the possible contact surfaces, and selects the most suitable contact surface as the surface is in contact with the ground. For example, [16] employs UV-measurement to evaluate candidate bases, and [17] uses the combination of Random Forest and Support Vector Machine to select the best base. These methods are suitable for objects of large contact area with the ground.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

OrienNormNet: Orientation Normalization of 3D Body Models

ZHAO¹,

DAI²,

HU³

et al. 2022

Proceedings of 3DBODY.TECH 2022 - 13th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

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3D human body models are widely used in human-centric industrial applications, including healthcare, fashion design, body biometrics extraction, and computer animation. Prior to processing and analyzing body models, it is significantly important to rotate them to the same orientation. For instance, body measurement systems and virtual try-on systems usually assume the orientation of the body is known. These systems will output incorrect results or report errors without the correct orientation information. Unfortunately, the orientations of scanned bodies are different in practice since they are in different coordinate systems due to the setup variations of scanners. To automatically normalize the orientations of bodies is a challenging task due to the presence of pose variations, noises, and holes during 3D body scanning. In this study, we propose a novel deep learning-based method dubbed OrienNormNet for normalizing the orientation of 3D body models. As shown in Figure 1 and Figure 2, OrienNormNet directly consumes raw point clouds or mesh vertices, and it is applied in an iterative manner. First, the centroids of point clouds are translated to the origin to obtain zero-centered point clouds. Next, OrienNormNet consumes zero-centered raw point clouds and outputs coarse axis rotation angles. Finally, OrienNormNet takes the coarsely rotated point clouds from the previous processing as an updated input and outputs the refined axis rotation angles. By applying the obtained coarse and fine axis rotation angles, thousands of bodies can be adjusted to the same orientation in a few seconds. Experimental results based on synthetic datasets as well as real-world datasets validated the effectiveness of our idea.

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“…We use Eq. 16 and Eq.18 to analyze the errors of the rotation angles along the three coordinate axes one by one to evaluate the performance of our method along each coordinate axis. Fig.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OrienNormNet: Orientation Normalization of 3D Body Models

ZHAO¹,

DAI²,

HU³

et al. 2022

Proceedings of 3DBODY.TECH 2022 - 13th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

View full text Add to dashboard Cite

show abstract

“…In Fu et al . [8] and Lin & Tai [22], upright orientation is estimated using supporting base candidates on which a 3D model can stand upright. These methods work well for most of the man-made models while not applicable to natural objects whose supporting bases are not well defined.…”

Section: Related Workmentioning

confidence: 99%

Upright orientation of 3D shapes with Convolutional Networks

Liu¹,

Zhang²,

Liu³

2016

Graphical Models

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Posing objects in their upright orientations is the very first step of 3D shape analysis. However, 3D models in existing repositories may be far from their right orientations due to various reasons. In this paper, we present a data-driven method for 3D object upright orientation estimation using 3D Convolutional Networks (Con-vNets), and the method is designed in the style of divide-and-conquer due to the interference effect. Thanks to the public big 3D datasets and the feature learning ability of ConvNets, our method can handle not only man-made objects but also natural ones. Besides, without any regularity assumptions, our method can deal with asymmetric and several other failure cases of existing approaches. Furthermore, a distance based clustering technique is proposed to reduce the memory cost and a test-time augmentation procedure is used to improve the accuracy. Its efficiency and effectiveness are demonstrated in the experimental results.

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