27,801 research outputs found
Design and Analysis of a Single-Camera Omnistereo Sensor for Quadrotor Micro Aerial Vehicles (MAVs)
We describe the design and 3D sensing performance of an omnidirectional stereo (omnistereo) vision system applied to Micro Aerial Vehicles (MAVs). The proposed omnistereo sensor employs a monocular camera that is co-axially aligned with a pair of hyperboloidal mirrors (a vertically-folded catadioptric configuration). We show that this arrangement provides a compact solution for omnidirectional 3D perception while mounted on top of propeller-based MAVs (not capable of large payloads). The theoretical single viewpoint (SVP) constraint helps us derive analytical solutions for the sensor’s projective geometry and generate SVP-compliant panoramic images to compute 3D information from stereo correspondences (in a truly synchronous fashion). We perform an extensive analysis on various system characteristics such as its size, catadioptric spatial resolution, field-of-view. In addition, we pose a probabilistic model for the uncertainty estimation of 3D information from triangulation of back-projected rays. We validate the projection error of the design using both synthetic and real-life images against ground-truth data. Qualitatively, we show 3D point clouds (dense and sparse) resulting out of a single image captured from a real-life experiment. We expect the reproducibility of our sensor as its model parameters can be optimized to satisfy other catadioptric-based omnistereo vision under different circumstances
On-device Scalable Image-based Localization via Prioritized Cascade Search and Fast One-Many RANSAC.
We present the design of an entire on-device system for large-scale urban
localization using images. The proposed design integrates compact image
retrieval and 2D-3D correspondence search to estimate the location in extensive
city regions. Our design is GPS agnostic and does not require network
connection. In order to overcome the resource constraints of mobile devices, we
propose a system design that leverages the scalability advantage of image
retrieval and accuracy of 3D model-based localization. Furthermore, we propose
a new hashing-based cascade search for fast computation of 2D-3D
correspondences. In addition, we propose a new one-many RANSAC for accurate
pose estimation. The new one-many RANSAC addresses the challenge of repetitive
building structures (e.g. windows, balconies) in urban localization. Extensive
experiments demonstrate that our 2D-3D correspondence search achieves
state-of-the-art localization accuracy on multiple benchmark datasets.
Furthermore, our experiments on a large Google Street View (GSV) image dataset
show the potential of large-scale localization entirely on a typical mobile
device
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
We propose a unified formulation for the problem of 3D human pose estimation
from a single raw RGB image that reasons jointly about 2D joint estimation and
3D pose reconstruction to improve both tasks. We take an integrated approach
that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN
architecture and uses the knowledge of plausible 3D landmark locations to
refine the search for better 2D locations. The entire process is trained
end-to-end, is extremely efficient and obtains state- of-the-art results on
Human3.6M outperforming previous approaches both on 2D and 3D errors.Comment: Paper presented at CVPR 1
Learning single-image 3D reconstruction by generative modelling of shape, pose and shading
We present a unified framework tackling two problems: class-specific 3D
reconstruction from a single image, and generation of new 3D shape samples.
These tasks have received considerable attention recently; however, most
existing approaches rely on 3D supervision, annotation of 2D images with
keypoints or poses, and/or training with multiple views of each object
instance. Our framework is very general: it can be trained in similar settings
to existing approaches, while also supporting weaker supervision. Importantly,
it can be trained purely from 2D images, without pose annotations, and with
only a single view per instance. We employ meshes as an output representation,
instead of voxels used in most prior work. This allows us to reason over
lighting parameters and exploit shading information during training, which
previous 2D-supervised methods cannot. Thus, our method can learn to generate
and reconstruct concave object classes. We evaluate our approach in various
settings, showing that: (i) it learns to disentangle shape from pose and
lighting; (ii) using shading in the loss improves performance compared to just
silhouettes; (iii) when using a standard single white light, our model
outperforms state-of-the-art 2D-supervised methods, both with and without pose
supervision, thanks to exploiting shading cues; (iv) performance improves
further when using multiple coloured lights, even approaching that of
state-of-the-art 3D-supervised methods; (v) shapes produced by our model
capture smooth surfaces and fine details better than voxel-based approaches;
and (vi) our approach supports concave classes such as bathtubs and sofas,
which methods based on silhouettes cannot learn.Comment: Extension of arXiv:1807.09259, accepted to IJCV. Differentiable
renderer available at https://github.com/pmh47/dir
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