414 research outputs found
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs
Human visual system relies on both binocular stereo cues and monocular
focusness cues to gain effective 3D perception. In computer vision, the two
problems are traditionally solved in separate tracks. In this paper, we present
a unified learning-based technique that simultaneously uses both types of cues
for depth inference. Specifically, we use a pair of focal stacks as input to
emulate human perception. We first construct a comprehensive focal stack
training dataset synthesized by depth-guided light field rendering. We then
construct three individual networks: a Focus-Net to extract depth from a single
focal stack, a EDoF-Net to obtain the extended depth of field (EDoF) image from
the focal stack, and a Stereo-Net to conduct stereo matching. We show how to
integrate them into a unified BDfF-Net to obtain high-quality depth maps.
Comprehensive experiments show that our approach outperforms the
state-of-the-art in both accuracy and speed and effectively emulates human
vision systems
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
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