18,673 research outputs found
On Rendering Synthetic Images for Training an Object Detector
We propose a novel approach to synthesizing images that are effective for
training object detectors. Starting from a small set of real images, our
algorithm estimates the rendering parameters required to synthesize similar
images given a coarse 3D model of the target object. These parameters can then
be reused to generate an unlimited number of training images of the object of
interest in arbitrary 3D poses, which can then be used to increase
classification performances.
A key insight of our approach is that the synthetically generated images
should be similar to real images, not in terms of image quality, but rather in
terms of features used during the detector training. We show in the context of
drone, plane, and car detection that using such synthetically generated images
yields significantly better performances than simply perturbing real images or
even synthesizing images in such way that they look very realistic, as is often
done when only limited amounts of training data are available
Aperture Supervision for Monocular Depth Estimation
We present a novel method to train machine learning algorithms to estimate
scene depths from a single image, by using the information provided by a
camera's aperture as supervision. Prior works use a depth sensor's outputs or
images of the same scene from alternate viewpoints as supervision, while our
method instead uses images from the same viewpoint taken with a varying camera
aperture. To enable learning algorithms to use aperture effects as supervision,
we introduce two differentiable aperture rendering functions that use the input
image and predicted depths to simulate the depth-of-field effects caused by
real camera apertures. We train a monocular depth estimation network end-to-end
to predict the scene depths that best explain these finite aperture images as
defocus-blurred renderings of the input all-in-focus image.Comment: To appear at CVPR 2018 (updated to camera ready version
Unsupervised Training for 3D Morphable Model Regression
We present a method for training a regression network from image pixels to 3D
morphable model coordinates using only unlabeled photographs. The training loss
is based on features from a facial recognition network, computed on-the-fly by
rendering the predicted faces with a differentiable renderer. To make training
from features feasible and avoid network fooling effects, we introduce three
objectives: a batch distribution loss that encourages the output distribution
to match the distribution of the morphable model, a loopback loss that ensures
the network can correctly reinterpret its own output, and a multi-view identity
loss that compares the features of the predicted 3D face and the input
photograph from multiple viewing angles. We train a regression network using
these objectives, a set of unlabeled photographs, and the morphable model
itself, and demonstrate state-of-the-art results.Comment: CVPR 2018 version with supplemental material
(http://openaccess.thecvf.com/content_cvpr_2018/html/Genova_Unsupervised_Training_for_CVPR_2018_paper.html
Evaluating methods for controlling depth perception in stereoscopic cinematography.
Existing stereoscopic imaging algorithms can create static stereoscopic images with perceived depth control function to ensure a compelling 3D viewing experience without visual discomfort. However, current algorithms do not normally support standard Cinematic Storytelling techniques. These techniques, such as object movement, camera motion, and zooming, can result in dynamic scene depth change within and between a series of frames (shots) in stereoscopic cinematography. In this study, we empirically evaluate the following three types of stereoscopic imaging approaches that aim to address this problem. (1) Real-Eye Configuration: set camera separation equal to the nominal human eye interpupillary distance. The perceived depth on the display is identical to the scene depth without any distortion. (2) Mapping Algorithm: map the scene depth to a predefined range on the display to avoid excessive perceived depth. A new method that dynamically adjusts the depth mapping from scene space to display space is presented in addition to an existing fixed depth mapping method. (3) Depth of Field Simulation: apply Depth of Field (DOF) blur effect to stereoscopic images. Only objects that are inside the DOF are viewed in full sharpness. Objects that are far away from the focus plane are blurred. We performed a human-based trial using the ITU-R BT.500-11 Recommendation to compare the depth quality of stereoscopic video sequences generated by the above-mentioned imaging methods. Our results indicate that viewers' practical 3D viewing volumes are different for individual stereoscopic displays and viewers can cope with much larger perceived depth range in viewing stereoscopic cinematography in comparison to static stereoscopic images. Our new dynamic depth mapping method does have an advantage over the fixed depth mapping method in controlling stereo depth perception. The DOF blur effect does not provide the expected improvement for perceived depth quality control in 3D cinematography. We anticipate the results will be of particular interest to 3D filmmaking and real time computer games
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