7 research outputs found
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
We propose a real-time RGB-based pipeline for object detection and 6D pose
estimation. Our novel 3D orientation estimation is based on a variant of the
Denoising Autoencoder that is trained on simulated views of a 3D model using
Domain Randomization. This so-called Augmented Autoencoder has several
advantages over existing methods: It does not require real, pose-annotated
training data, generalizes to various test sensors and inherently handles
object and view symmetries. Instead of learning an explicit mapping from input
images to object poses, it provides an implicit representation of object
orientations defined by samples in a latent space. Our pipeline achieves
state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D
domain. We also evaluate on the LineMOD dataset where we can compete with other
synthetically trained approaches. We further increase performance by correcting
3D orientation estimates to account for perspective errors when the object
deviates from the image center and show extended results.Comment: Code available at: https://github.com/DLR-RM/AugmentedAutoencode
Recovering 6D Object Pose: A Review and Multi-modal Analysis
A large number of studies analyse object detection and pose estimation at
visual level in 2D, discussing the effects of challenges such as occlusion,
clutter, texture, etc., on the performances of the methods, which work in the
context of RGB modality. Interpreting the depth data, the study in this paper
presents thorough multi-modal analyses. It discusses the above-mentioned
challenges for full 6D object pose estimation in RGB-D images comparing the
performances of several 6D detectors in order to answer the following
questions: What is the current position of the computer vision community for
maintaining "automation" in robotic manipulation? What next steps should the
community take for improving "autonomy" in robotics while handling objects? Our
findings include: (i) reasonably accurate results are obtained on
textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy
existence of occlusion and clutter severely affects the detectors, and
similar-looking distractors is the biggest challenge in recovering instances'
6D. (iii) Template-based methods and random forest-based learning algorithms
underlie object detection and 6D pose estimation. Recent paradigm is to learn
deep discriminative feature representations and to adopt CNNs taking RGB images
as input. (iv) Depending on the availability of large-scale 6D annotated depth
datasets, feature representations can be learnt on these datasets, and then the
learnt representations can be customized for the 6D problem
Relative Pose from Deep Learned Depth and a Single Affine Correspondence
We propose a new approach for combining deep-learned non-metric monocular
depth with affine correspondences (ACs) to estimate the relative pose of two
calibrated cameras from a single correspondence. Considering the depth
information and affine features, two new constraints on the camera pose are
derived. The proposed solver is usable within 1-point RANSAC approaches. Thus,
the processing time of the robust estimation is linear in the number of
correspondences and, therefore, orders of magnitude faster than by using
traditional approaches. The proposed 1AC+D solver is tested both on synthetic
data and on 110395 publicly available real image pairs where we used an
off-the-shelf monocular depth network to provide up-to-scale depth per pixel.
The proposed 1AC+D leads to similar accuracy as traditional approaches while
being significantly faster. When solving large-scale problems, e.g., pose-graph
initialization for Structure-from-Motion (SfM) pipelines, the overhead of
obtaining ACs and monocular depth is negligible compared to the speed-up gained
in the pairwise geometric verification, i.e., relative pose estimation. This is
demonstrated on scenes from the 1DSfM dataset using a state-of-the-art global
SfM algorithm. Source code: https://github.com/eivan/one-ac-pos
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries. Instead of learning an explicit mapping from input images to object poses, it provides an implicit representation of object orientations defined by samples in a latent space. Experiments on the T-LESS and LineMOD datasets show that our method outperforms similar model-based approaches and competes with state-of-the art approaches that require real pose-annotated images
Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images
© 2020, Springer Nature Switzerland AG. Recent methods for 6D pose estimation of objects assume either textured 3D models or real images that cover the entire range of target poses. However, it is difficult to obtain textured 3D models and annotate the poses of objects in real scenarios. This paper proposes a method, Neural Object Learning (NOL), that creates synthetic images of objects in arbitrary poses by combining only a few observations from cluttered images. A novel refinement step is proposed to align inaccurate poses of objects in source images, which results in better quality images. Evaluations performed on two public datasets show that the rendered images created by NOL lead to state-of-the-art performance in comparison to methods that use 13 times the number of real images. Evaluations on our new dataset show multiple objects can be trained and recognized simultaneously using a sequence of a fixed scene