133 research outputs found
6D Pose Estimation using an Improved Method based on Point Pair Features
The Point Pair Feature (Drost et al. 2010) has been one of the most
successful 6D pose estimation method among model-based approaches as an
efficient, integrated and compromise alternative to the traditional local and
global pipelines. During the last years, several variations of the algorithm
have been proposed. Among these extensions, the solution introduced by
Hinterstoisser et al. (2016) is a major contribution. This work presents a
variation of this PPF method applied to the SIXD Challenge datasets presented
at the 3rd International Workshop on Recovering 6D Object Pose held at the ICCV
2017. We report an average recall of 0.77 for all datasets and overall recall
of 0.82, 0.67, 0.85, 0.37, 0.97 and 0.96 for hinterstoisser, tless, tudlight,
rutgers, tejani and doumanoglou datasets, respectively
Accurate 6D Object Pose Estimation by Pose Conditioned Mesh Reconstruction
Current 6D object pose methods consist of deep CNN models fully optimized for
a single object but with its architecture standardized among objects with
different shapes. In contrast to previous works, we explicitly exploit each
object's distinct topological information i.e. 3D dense meshes in the pose
estimation model, with an automated process and prior to any post-processing
refinement stage. In order to achieve this, we propose a learning framework in
which a Graph Convolutional Neural Network reconstructs a pose conditioned 3D
mesh of the object. A robust estimation of the allocentric orientation is
recovered by computing, in a differentiable manner, the Procrustes' alignment
between the canonical and reconstructed dense 3D meshes. 6D egocentric pose is
then lifted using additional mask and 2D centroid projection estimations. Our
method is capable of self validating its pose estimation by measuring the
quality of the reconstructed mesh, which is invaluable in real life
applications. In our experiments on the LINEMOD, OCCLUSION and YCB-Video
benchmarks, the proposed method outperforms state-of-the-arts
A Survey on Joint Object Detection and Pose Estimation using Monocular Vision
In this survey we present a complete landscape of joint object detection and
pose estimation methods that use monocular vision. Descriptions of traditional
approaches that involve descriptors or models and various estimation methods
have been provided. These descriptors or models include chordiograms,
shape-aware deformable parts model, bag of boundaries, distance transform
templates, natural 3D markers and facet features whereas the estimation methods
include iterative clustering estimation, probabilistic networks and iterative
genetic matching. Hybrid approaches that use handcrafted feature extraction
followed by estimation by deep learning methods have been outlined. We have
investigated and compared, wherever possible, pure deep learning based
approaches (single stage and multi stage) for this problem. Comprehensive
details of the various accuracy measures and metrics have been illustrated. For
the purpose of giving a clear overview, the characteristics of relevant
datasets are discussed. The trends that prevailed from the infancy of this
problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and
Pattern Recognition (CCVPR) 201
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