32,788 research outputs found
Point Pair Feature based Object Detection for Random Bin Picking
Point pair features are a popular representation for free form 3D object
detection and pose estimation. In this paper, their performance in an
industrial random bin picking context is investigated. A new method to generate
representative synthetic datasets is proposed. This allows to investigate the
influence of a high degree of clutter and the presence of self similar
features, which are typical to our application. We provide an overview of
solutions proposed in literature and discuss their strengths and weaknesses. A
simple heuristic method to drastically reduce the computational complexity is
introduced, which results in improved robustness, speed and accuracy compared
to the naive approach
Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data
Reliable human pose estimation (HPE) is essential to many clinical
applications, such as surgical workflow analysis, radiation safety monitoring
and human-robot cooperation. Proposed methods for the operating room (OR) rely
either on foreground estimation using a multi-camera system, which is a
challenge in real ORs due to color similarities and frequent illumination
changes, or on wearable sensors or markers, which are invasive and therefore
difficult to introduce in the room. Instead, we propose a novel approach based
on Pictorial Structures (PS) and on RGB-D data, which can be easily deployed in
real ORs. We extend the PS framework in two ways. First, we build robust and
discriminative part detectors using both color and depth images. We also
present a novel descriptor for depth images, called histogram of depth
differences (HDD). Second, we extend PS to 3D by proposing 3D pairwise
constraints and a new method that makes exact inference tractable. Our approach
is evaluated for pose estimation and clinician detection on a challenging RGB-D
dataset recorded in a busy operating room during live surgeries. We conduct
series of experiments to study the different part detectors in conjunction with
the various 2D or 3D pairwise constraints. Our comparisons demonstrate that 3D
PS with RGB-D part detectors significantly improves the results in a visually
challenging operating environment.Comment: The supplementary video is available at https://youtu.be/iabbGSqRSg
3D Model Assisted Image Segmentation
The problem of segmenting a given image into coherent regions is important in Computer Vision and many industrial applications require segmenting a known object into its components. Examples include identifying individual parts of a component for proces
Lifting GIS Maps into Strong Geometric Context for Scene Understanding
Contextual information can have a substantial impact on the performance of
visual tasks such as semantic segmentation, object detection, and geometric
estimation. Data stored in Geographic Information Systems (GIS) offers a rich
source of contextual information that has been largely untapped by computer
vision. We propose to leverage such information for scene understanding by
combining GIS resources with large sets of unorganized photographs using
Structure from Motion (SfM) techniques. We present a pipeline to quickly
generate strong 3D geometric priors from 2D GIS data using SfM models aligned
with minimal user input. Given an image resectioned against this model, we
generate robust predictions of depth, surface normals, and semantic labels. We
show that the precision of the predicted geometry is substantially more
accurate other single-image depth estimation methods. We then demonstrate the
utility of these contextual constraints for re-scoring pedestrian detections,
and use these GIS contextual features alongside object detection score maps to
improve a CRF-based semantic segmentation framework, boosting accuracy over
baseline models
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
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects
We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e.
translation and rotation, of texture-less rigid objects. The dataset features
thirty industry-relevant objects with no significant texture and no
discriminative color or reflectance properties. The objects exhibit symmetries
and mutual similarities in shape and/or size. Compared to other datasets, a
unique property is that some of the objects are parts of others. The dataset
includes training and test images that were captured with three synchronized
sensors, specifically a structured-light and a time-of-flight RGB-D sensor and
a high-resolution RGB camera. There are approximately 39K training and 10K test
images from each sensor. Additionally, two types of 3D models are provided for
each object, i.e. a manually created CAD model and a semi-automatically
reconstructed one. Training images depict individual objects against a black
background. Test images originate from twenty test scenes having varying
complexity, which increases from simple scenes with several isolated objects to
very challenging ones with multiple instances of several objects and with a
high amount of clutter and occlusion. The images were captured from a
systematically sampled view sphere around the object/scene, and are annotated
with accurate ground truth 6D poses of all modeled objects. Initial evaluation
results indicate that the state of the art in 6D object pose estimation has
ample room for improvement, especially in difficult cases with significant
occlusion. The T-LESS dataset is available online at cmp.felk.cvut.cz/t-less.Comment: WACV 201
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