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Brain network mechanisms of visual shape completion [PDF]
NeuroImage, 2021 Visual shape completion recovers object shape, size, and number from spatially segregated edges. Despite being extensively investigated, the process's underlying brain regions, networks, and functional connections are still not well understood.
Brian P. Keane +5 more
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Contrastive Learning for 3D Point Clouds Classification and Shape Completion [PDF]
Sensors, 2021 In this paper, we present the idea of Self Supervised learning on the shape completion and classification of point clouds. Most 3D shape completion pipelines utilize AutoEncoders to extract features from point clouds used in downstream tasks such as ...
Danish Nazir +4 more
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Neural shape completion for personalized Maxillofacial surgery [PDF]
Scientific ReportsIn this paper, we investigate the effectiveness of shape completion neural networks as clinical aids in maxillofacial surgery planning. We present a pipeline to apply shape completion networks to automatically reconstruct complete eumorphic 3D meshes ...
Stefano Mazzocchetti +6 more
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SELF-SUPERVISED ADVERSARIAL SHAPE COMPLETION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 The goal of this paper is 3D shape completion: given an incomplete instance of a known category, hallucinate a complete version of it that is geometrically plausible. We develop an adversarial framework that makes it possible to learn shape completion in
T. Peters, K. Schindler, C. Brenner
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Anisotropic SpiralNet for 3D Shape Completion and Denoising [PDF]
Sensors, 2022 Three-dimensional mesh post-processing is an important task because low-precision hardware and a poor capture environment will inevitably lead to unordered point clouds with unwanted noise and holes that should be suitably corrected while preserving the ...
Seong Uk Kim +3 more
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Sparse convolutional neural network for high-resolution skull shape completion and shape super-resolution [PDF]
Scientific Reports, 2023 Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of high-resolution shapes ...
Jianning Li +5 more
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Shape Completion Using Deep Boltzmann Machine. [PDF]
Comput Intell Neurosci, 2017 Shape completion is an important task in the field of image processing. An alternative method is to capture the shape information and finish the completion by a generative model, such as Deep Boltzmann Machine. With its powerful ability to deal with the distribution of the shapes, it is quite easy to acquire the result by sampling from the model.
Wang Z, Wu Q.
europepmc +4 more sources
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input shape, our method first infers a low-resolution -- but complete -- output ...
Dai, Angela +2 more
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SurgPointTransformer: transformer-based vertebra shape completion using RGB-D imaging [PDF]
Computer Assisted SurgeryState-of-the-art computer- and robot-assisted surgery systems rely on intraoperative imaging technologies such as computed tomography and fluoroscopy to provide detailed 3D visualizations of patient anatomy.
Aidana Massalimova +5 more
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Log-Aesthetic Curves for Shape Completion Problem [PDF]
Journal of Applied Mathematics, 2014 An object with complete boundary or silhouette is essential in various design and computer graphics feats. Due to various reasons, some parts of the object can be missing hence increasing the complexity in designing process.
R. U. Gobithaasan +2 more
doaj +3 more sources