56 research outputs found
iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects
We address the task of 6D pose estimation of known rigid objects from single
input images in scenarios where the objects are partly occluded. Recent
RGB-D-based methods are robust to moderate degrees of occlusion. For RGB
inputs, no previous method works well for partly occluded objects. Our main
contribution is to present the first deep learning-based system that estimates
accurate poses for partly occluded objects from RGB-D and RGB input. We achieve
this with a new instance-aware pipeline that decomposes 6D object pose
estimation into a sequence of simpler steps, where each step removes specific
aspects of the problem. The first step localizes all known objects in the image
using an instance segmentation network, and hence eliminates surrounding
clutter and occluders. The second step densely maps pixels to 3D object surface
positions, so called object coordinates, using an encoder-decoder network, and
hence eliminates object appearance. The third, and final, step predicts the 6D
pose using geometric optimization. We demonstrate that we significantly
outperform the state-of-the-art for pose estimation of partly occluded objects
for both RGB and RGB-D input
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
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Generative models that learn disentangled representations for different
factors of variation in an image can be very useful for targeted data
augmentation. By sampling from the disentangled latent subspace of interest, we
can efficiently generate new data necessary for a particular task. Learning
disentangled representations is a challenging problem, especially when certain
factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary
subspaces by using only weak supervision in form of pairwise similarity labels.
Inspired by the recent success of cycle-consistent adversarial architectures,
we use cycle-consistency in a variational auto-encoder framework. Our
non-adversarial approach is in contrast with the recent works that combine
adversarial training with auto-encoders to disentangle representations. We show
compelling results of disentangled latent subspaces on three datasets and
compare with recent works that leverage adversarial training
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
Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European
Conference on Computer Vision (ECCV) 2016;
https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
Current object recognition methods fail on object sets that include both
diffuse, reflective and transparent materials, although they are very common in
domestic scenarios. We show that a combination of cues from multiple sensor
modalities, including specular reflectance and unavailable depth information,
allows us to capture a larger subset of household objects by extending a state
of the art object recognition method. This leads to a significant increase in
robustness of recognition over a larger set of commonly used objects.Comment: 12 page
Real-time 3D Tracking of Articulated Tools for Robotic Surgery
In robotic surgery, tool tracking is important for providing safe tool-tissue
interaction and facilitating surgical skills assessment. Despite recent
advances in tool tracking, existing approaches are faced with major
difficulties in real-time tracking of articulated tools. Most algorithms are
tailored for offline processing with pre-recorded videos. In this paper, we
propose a real-time 3D tracking method for articulated tools in robotic
surgery. The proposed method is based on the CAD model of the tools as well as
robot kinematics to generate online part-based templates for efficient 2D
matching and 3D pose estimation. A robust verification approach is incorporated
to reject outliers in 2D detections, which is then followed by fusing inliers
with robot kinematic readings for 3D pose estimation of the tool. The proposed
method has been validated with phantom data, as well as ex vivo and in vivo
experiments. The results derived clearly demonstrate the performance advantage
of the proposed method when compared to the state-of-the-art.Comment: This paper was presented in MICCAI 2016 conference, and a DOI was
linked to the publisher's versio
Virtual Immortality: Reanimating Characters from TV Shows.
The objective of this work is to build virtual talking avatars of characters fully automatically from TV shows. From this unconstrained data, we show how to capture a character's style of speech, visual appearance and language in an e ort to construct an interactive avatar of the person and e ectively immortalize them in a computational model. We make three contributions (i) a complete framework for producing a generative model of the audiovisual and language of characters from TV shows; (ii) a novel method for aligning transcripts to video using the audio; and (iii) a fast audio segmentation system for silencing nonspoken audio from TV shows. Our framework is demonstrated using all 236 episodes from the TV series Friends [34] ( 97hrs of video) and shown to generate novel sentences as well as character specific speech and video
SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection
Freespace detection is an essential component of visual perception for
self-driving cars. The recent efforts made in data-fusion convolutional neural
networks (CNNs) have significantly improved semantic driving scene
segmentation. Freespace can be hypothesized as a ground plane, on which the
points have similar surface normals. Hence, in this paper, we first introduce a
novel module, named surface normal estimator (SNE), which can infer surface
normal information from dense depth/disparity images with high accuracy and
efficiency. Furthermore, we propose a data-fusion CNN architecture, referred to
as RoadSeg, which can extract and fuse features from both RGB images and the
inferred surface normal information for accurate freespace detection. For
research purposes, we publish a large-scale synthetic freespace detection
dataset, named Ready-to-Drive (R2D) road dataset, collected under different
illumination and weather conditions. The experimental results demonstrate that
our proposed SNE module can benefit all the state-of-the-art CNNs for freespace
detection, and our SNE-RoadSeg achieves the best overall performance among
different datasets.Comment: ECCV 202
Single-Image Depth Prediction Makes Feature Matching Easier
Good local features improve the robustness of many 3D re-localization and
multi-view reconstruction pipelines. The problem is that viewing angle and
distance severely impact the recognizability of a local feature. Attempts to
improve appearance invariance by choosing better local feature points or by
leveraging outside information, have come with pre-requisites that made some of
them impractical. In this paper, we propose a surprisingly effective
enhancement to local feature extraction, which improves matching. We show that
CNN-based depths inferred from single RGB images are quite helpful, despite
their flaws. They allow us to pre-warp images and rectify perspective
distortions, to significantly enhance SIFT and BRISK features, enabling more
good matches, even when cameras are looking at the same scene but in opposite
directions.Comment: 14 pages, 7 figures, accepted for publication at the European
conference on computer vision (ECCV) 202
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