18,934 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
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
Harvesting Multiple Views for Marker-less 3D Human Pose Annotations
Recent advances with Convolutional Networks (ConvNets) have shifted the
bottleneck for many computer vision tasks to annotated data collection. In this
paper, we present a geometry-driven approach to automatically collect
annotations for human pose prediction tasks. Starting from a generic ConvNet
for 2D human pose, and assuming a multi-view setup, we describe an automatic
way to collect accurate 3D human pose annotations. We capitalize on constraints
offered by the 3D geometry of the camera setup and the 3D structure of the
human body to probabilistically combine per view 2D ConvNet predictions into a
globally optimal 3D pose. This 3D pose is used as the basis for harvesting
annotations. The benefit of the annotations produced automatically with our
approach is demonstrated in two challenging settings: (i) fine-tuning a generic
ConvNet-based 2D pose predictor to capture the discriminative aspects of a
subject's appearance (i.e.,"personalization"), and (ii) training a ConvNet from
scratch for single view 3D human pose prediction without leveraging 3D pose
groundtruth. The proposed multi-view pose estimator achieves state-of-the-art
results on standard benchmarks, demonstrating the effectiveness of our method
in exploiting the available multi-view information.Comment: CVPR 2017 Camera Read
Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
We present a method for learning an embedding that places images of humans in
similar poses nearby. This embedding can be used as a direct method of
comparing images based on human pose, avoiding potential challenges of
estimating body joint positions. Pose embedding learning is formulated under a
triplet-based distance criterion. A deep architecture is used to allow learning
of a representation capable of making distinctions between different poses.
Experiments on human pose matching and retrieval from video data demonstrate
the potential of the method
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