3,968 research outputs found
Generic 3D Representation via Pose Estimation and Matching
Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose estimation and wide baseline feature matching. Our
method is based upon the premise that by providing supervision over a set of
carefully selected foundational tasks, generalization to novel tasks and
abstraction capabilities can be achieved. We empirically show that the internal
representation of a multi-task ConvNet trained to solve the above core problems
generalizes to novel 3D tasks (e.g., scene layout estimation, object pose
estimation, surface normal estimation) without the need for fine-tuning and
shows traits of abstraction abilities (e.g., cross-modality pose estimation).
In the context of the core supervised tasks, we demonstrate our representation
achieves state-of-the-art wide baseline feature matching results without
requiring apriori rectification (unlike SIFT and the majority of learned
features). We also show 6DOF camera pose estimation given a pair local image
patches. The accuracy of both supervised tasks come comparable to humans.
Finally, we contribute a large-scale dataset composed of object-centric street
view scenes along with point correspondences and camera pose information, and
conclude with a discussion on the learned representation and open research
questions.Comment: Published in ECCV16. See the project website
http://3drepresentation.stanford.edu/ and dataset website
https://github.com/amir32002/3D_Street_Vie
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
Autonomous Robotic System using Non-Destructive Evaluation methods for Bridge Deck Inspection
Bridge condition assessment is important to maintain the quality of highway
roads for public transport. Bridge deterioration with time is inevitable due to
aging material, environmental wear and in some cases, inadequate maintenance.
Non-destructive evaluation (NDE) methods are preferred for condition assessment
for bridges, concrete buildings, and other civil structures. Some examples of
NDE methods are ground penetrating radar (GPR), acoustic emission, and
electrical resistivity (ER). NDE methods provide the ability to inspect a
structure without causing any damage to the structure in the process. In
addition, NDE methods typically cost less than other methods, since they do not
require inspection sites to be evacuated prior to inspection, which greatly
reduces the cost of safety related issues during the inspection process. In
this paper, an autonomous robotic system equipped with three different NDE
sensors is presented. The system employs GPR, ER, and a camera for data
collection. The system is capable of performing real-time, cost-effective
bridge deck inspection, and is comprised of a mechanical robot design and
machine learning and pattern recognition methods for automated steel rebar
picking to provide realtime condition maps of the corrosive deck environments
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