13,642 research outputs found
Grasping bulky objects with two anthropomorphic hands
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents an algorithm to compute precision grasps for bulky objects using two anthropomorphic hands. We use objects modeled as point clouds obtained from a sensor camera or from a CAD model. We then process the point clouds dividing them into two set of slices where we look for sets of triplets of points. Each triplet must accomplish some physical conditions based on the structure of the hands. Then, the triplets of points from each set of slices are evaluated to find a combination that satisfies the force closure condition (FC). Once one valid couple of triplets have been found the inverse kinematics of the system is computed in order to know if the corresponding points are reachable by the hands, if so, motion planning and a collision check are performed to asses if the final grasp configuration of the system is suitable. The paper
inclu des some application examples of the proposed approachAccepted versio
Deep Depth Completion of a Single RGB-D Image
The goal of our work is to complete the depth channel of an RGB-D image.
Commodity-grade depth cameras often fail to sense depth for shiny, bright,
transparent, and distant surfaces. To address this problem, we train a deep
network that takes an RGB image as input and predicts dense surface normals and
occlusion boundaries. Those predictions are then combined with raw depth
observations provided by the RGB-D camera to solve for depths for all pixels,
including those missing in the original observation. This method was chosen
over others (e.g., inpainting depths directly) as the result of extensive
experiments with a new depth completion benchmark dataset, where holes are
filled in training data through the rendering of surface reconstructions
created from multiview RGB-D scans. Experiments with different network inputs,
depth representations, loss functions, optimization methods, inpainting
methods, and deep depth estimation networks show that our proposed approach
provides better depth completions than these alternatives.Comment: Accepted by CVPR2018 (Spotlight). Project webpage:
http://deepcompletion.cs.princeton.edu/ This version includes supplementary
materials which provide more implementation details, quantitative evaluation,
and qualitative results. Due to file size limit, please check project website
for high-res pape
GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation
We present a learning-based method, namely GeoUDF,to tackle the long-standing
and challenging problem of reconstructing a discrete surface from a sparse
point cloud.To be specific, we propose a geometry-guided learning method for
UDF and its gradient estimation that explicitly formulates the unsigned
distance of a query point as the learnable affine averaging of its distances to
the tangent planes of neighboring points on the surface. Besides,we model the
local geometric structure of the input point clouds by explicitly learning a
quadratic polynomial for each point. This not only facilitates upsampling the
input sparse point cloud but also naturally induces unoriented normal, which
further augments UDF estimation. Finally, to extract triangle meshes from the
predicted UDF we propose a customized edge-based marching cube module. We
conduct extensive experiments and ablation studies to demonstrate the
significant advantages of our method over state-of-the-art methods in terms of
reconstruction accuracy, efficiency, and generality. The source code is
publicly available at https://github.com/rsy6318/GeoUDF
Optimization in Differentiable Manifolds in Order to Determine the Method of Construction of Prehistoric Wall-Paintings
In this paper a general methodology is introduced for the determination of
potential prototype curves used for the drawing of prehistoric wall-paintings.
The approach includes a) preprocessing of the wall-paintings contours to
properly partition them, according to their curvature, b) choice of prototype
curves families, c) analysis and optimization in 4-manifold for a first
estimation of the form of these prototypes, d) clustering of the contour parts
and the prototypes, to determine a minimal number of potential guides, e)
further optimization in 4-manifold, applied to each cluster separately, in
order to determine the exact functional form of the potential guides, together
with the corresponding drawn contour parts. The introduced methodology
simultaneously deals with two problems: a) the arbitrariness in data-points
orientation and b) the determination of one proper form for a prototype curve
that optimally fits the corresponding contour data. Arbitrariness in
orientation has been dealt with a novel curvature based error, while the proper
forms of curve prototypes have been exhaustively determined by embedding
curvature deformations of the prototypes into 4-manifolds. Application of this
methodology to celebrated wall-paintings excavated at Tyrins, Greece and the
Greek island of Thera, manifests it is highly probable that these
wall-paintings had been drawn by means of geometric guides that correspond to
linear spirals and hyperbolae. These geometric forms fit the drawings' lines
with an exceptionally low average error, less than 0.39mm. Hence, the approach
suggests the existence of accurate realizations of complicated geometric
entities, more than 1000 years before their axiomatic formulation in Classical
Ages
Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation
Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions
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