31,371 research outputs found
3D Pose Estimation and 3D Model Retrieval for Objects in the Wild
We propose a scalable, efficient and accurate approach to retrieve 3D models
for objects in the wild. Our contribution is twofold. We first present a 3D
pose estimation approach for object categories which significantly outperforms
the state-of-the-art on Pascal3D+. Second, we use the estimated pose as a prior
to retrieve 3D models which accurately represent the geometry of objects in RGB
images. For this purpose, we render depth images from 3D models under our
predicted pose and match learned image descriptors of RGB images against those
of rendered depth images using a CNN-based multi-view metric learning approach.
In this way, we are the first to report quantitative results for 3D model
retrieval on Pascal3D+, where our method chooses the same models as human
annotators for 50% of the validation images on average. In addition, we show
that our method, which was trained purely on Pascal3D+, retrieves rich and
accurate 3D models from ShapeNet given RGB images of objects in the wild.Comment: Accepted to Conference on Computer Vision and Pattern Recognition
(CVPR) 201
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
SuperPoint: Self-Supervised Interest Point Detection and Description
This paper presents a self-supervised framework for training interest point
detectors and descriptors suitable for a large number of multiple-view geometry
problems in computer vision. As opposed to patch-based neural networks, our
fully-convolutional model operates on full-sized images and jointly computes
pixel-level interest point locations and associated descriptors in one forward
pass. We introduce Homographic Adaptation, a multi-scale, multi-homography
approach for boosting interest point detection repeatability and performing
cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on
the MS-COCO generic image dataset using Homographic Adaptation, is able to
repeatedly detect a much richer set of interest points than the initial
pre-adapted deep model and any other traditional corner detector. The final
system gives rise to state-of-the-art homography estimation results on HPatches
when compared to LIFT, SIFT and ORB.Comment: Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM
Workshop (DL4VSLAM2018
Learning Descriptors for Object Recognition and 3D Pose Estimation
Detecting poorly textured objects and estimating their 3D pose reliably is
still a very challenging problem. We introduce a simple but powerful approach
to computing descriptors for object views that efficiently capture both the
object identity and 3D pose. By contrast with previous manifold-based
approaches, we can rely on the Euclidean distance to evaluate the similarity
between descriptors, and therefore use scalable Nearest Neighbor search methods
to efficiently handle a large number of objects under a large range of poses.
To achieve this, we train a Convolutional Neural Network to compute these
descriptors by enforcing simple similarity and dissimilarity constraints
between the descriptors. We show that our constraints nicely untangle the
images from different objects and different views into clusters that are not
only well-separated but also structured as the corresponding sets of poses: The
Euclidean distance between descriptors is large when the descriptors are from
different objects, and directly related to the distance between the poses when
the descriptors are from the same object. These important properties allow us
to outperform state-of-the-art object views representations on challenging RGB
and RGB-D data.Comment: CVPR 201
FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image
In this work, we introduce the novel problem of identifying dense canonical
3D coordinate frames from a single RGB image. We observe that each pixel in an
image corresponds to a surface in the underlying 3D geometry, where a canonical
frame can be identified as represented by three orthogonal axes, one along its
normal direction and two in its tangent plane. We propose an algorithm to
predict these axes from RGB. Our first insight is that canonical frames
computed automatically with recently introduced direction field synthesis
methods can provide training data for the task. Our second insight is that
networks designed for surface normal prediction provide better results when
trained jointly to predict canonical frames, and even better when trained to
also predict 2D projections of canonical frames. We conjecture this is because
projections of canonical tangent directions often align with local gradients in
images, and because those directions are tightly linked to 3D canonical frames
through projective geometry and orthogonality constraints. In our experiments,
we find that our method predicts 3D canonical frames that can be used in
applications ranging from surface normal estimation, feature matching, and
augmented reality
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