7 research outputs found
Modal-Graph 3D Shape Servoing of Deformable Objects with Raw Point Clouds
Deformable object manipulation (DOM) with point clouds has great potential as
non-rigid 3D shapes can be measured without detecting and tracking image
features. However, robotic shape control of deformable objects with point
clouds is challenging due to: the unknown point-wise correspondences and the
noisy partial observability of raw point clouds; the modeling difficulties of
the relationship between point clouds and robot motions. To tackle these
challenges, this paper introduces a novel modal-graph framework for the
model-free shape servoing of deformable objects with raw point clouds. Unlike
the existing works studying the object's geometry structure, our method builds
a low-frequency deformation structure for the DOM system, which is robust to
the measurement irregularities. The built modal representation and graph
structure enable us to directly extract low-dimensional deformation features
from raw point clouds. Such extraction requires no extra point processing of
registrations, refinements, and occlusion removal. Moreover, to shape the
object using the extracted features, we design an adaptive robust controller
which is proved to be input-to-state stable (ISS) without offline learning or
identifying both the physical and geometric object models. Extensive
simulations and experiments are conducted to validate the effectiveness of our
method for linear, planar, tubular, and solid objects under different settings
StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS
Most existing methods for category-level pose estimation rely on object point
clouds. However, when considering transparent objects, depth cameras are
usually not able to capture meaningful data, resulting in point clouds with
severe artifacts. Without a high-quality point cloud, existing methods are not
applicable to challenging transparent objects. To tackle this problem, we
present StereoPose, a novel stereo image framework for category-level object
pose estimation, ideally suited for transparent objects. For a robust
estimation from pure stereo images, we develop a pipeline that decouples
category-level pose estimation into object size estimation, initial pose
estimation, and pose refinement. StereoPose then estimates object pose based on
representation in the normalized object coordinate space~(NOCS). To address the
issue of image content aliasing, we further define a back-view NOCS map for the
transparent object. The back-view NOCS aims to reduce the network learning
ambiguity caused by content aliasing, and leverage informative cues on the back
of the transparent object for more accurate pose estimation. To further improve
the performance of the stereo framework, StereoPose is equipped with a parallax
attention module for stereo feature fusion and an epipolar loss for improving
the stereo-view consistency of network predictions. Extensive experiments on
the public TOD dataset demonstrate the superiority of the proposed StereoPose
framework for category-level 6D transparent object pose estimation.Comment: 7 pages, 6 figures, Project homepage:
https://appsrv.cse.cuhk.edu.hk/~kaichen/stereopose.htm
Parametric Human Body Model for Digital Apparel Design
With the advance in 3D body scanning technology, it opens opportunities for virtual try-on and automatic made-to-measure in apparel products domain. This paper proposed a novel feature-based parametric method of human body shape from the cloud points of 3D body scanner [TC]2. Firstly, we improved the skeleton construction through adding and adjusting the position of joints. Secondly, automatic extraction approach of semantic feature cross-sections is developed based on the hierarchy. According to the unique distribution of cloud points of each cross-section of each body part, the extraction method of key points on the cross-section is described. Thirdly, we presented an interpolation approach of key points which fit cardinal spline to cross-section for each body part, in which tension parameter is used to represent the simple deformation of body shape. Finally, a connection approach of body part is proposed by sharing a boundary curve. The proposed method has been tested with our virtual human model (VHM) system which is robust and easier to use. The process generally requires about five minutes for generating a full body model that represents the body shape captured by 3D body scanner. The model can be imported in a CAD environment for application to a wide variety of ergonomic analyses
Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker.
BackgroundDysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues.MethodsA subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels.ResultsMultimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%.ConclusionsAdolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity