1,008 research outputs found
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
Videos for outdoor scene often show unpleasant blur effects due to the large
relative motion between the camera and the dynamic objects and large depth
variations. Existing works typically focus monocular video deblurring. In this
paper, we propose a novel approach to deblurring from stereo videos. In
particular, we exploit the piece-wise planar assumption about the scene and
leverage the scene flow information to deblur the image. Unlike the existing
approach [31] which used a pre-computed scene flow, we propose a single
framework to jointly estimate the scene flow and deblur the image, where the
motion cues from scene flow estimation and blur information could reinforce
each other, and produce superior results than the conventional scene flow
estimation or stereo deblurring methods. We evaluate our method extensively on
two available datasets and achieve significant improvement in flow estimation
and removing the blur effect over the state-of-the-art methods.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences
The dense depth estimation of a 3D scene has numerous applications, mainly in
robotics and surveillance. LiDAR and radar sensors are the hardware solution
for real-time depth estimation, but these sensors produce sparse depth maps and
are sometimes unreliable. In recent years research aimed at tackling depth
estimation using single 2D image has received a lot of attention. The deep
learning based self-supervised depth estimation methods from the rectified
stereo and monocular video frames have shown promising results. We propose a
self-attention based depth and ego-motion network for unrectified images. We
also introduce non-differentiable distortion of the camera into the training
pipeline. Our approach performs competitively when compared to other
established approaches that used rectified images for depth estimation
- …