2 research outputs found
Continual Occlusions and Optical Flow Estimation
Two optical flow estimation problems are addressed: i) occlusion estimation
and handling, and ii) estimation from image sequences longer than two frames.
The proposed ContinualFlow method estimates occlusions before flow, avoiding
the use of flow corrupted by occlusions for their estimation. We show that
providing occlusion masks as an additional input to flow estimation improves
the standard performance metric by more than 25\% on both KITTI and Sintel. As
a second contribution, a novel method for incorporating information from past
frames into flow estimation is introduced. The previous frame flow serves as an
input to occlusion estimation and as a prior in occluded regions, i.e. those
without visual correspondences. By continually using the previous frame flow,
ContinualFlow performance improves further by 18\% on KITTI and 7\% on Sintel,
achieving top performance on KITTI and Sintel.Comment: ACCV2018, 16 page
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
We present a novel method for simultaneous learning of depth, egomotion,
object motion, and camera intrinsics from monocular videos, using only
consistency across neighboring video frames as supervision signal. Similarly to
prior work, our method learns by applying differentiable warping to frames and
comparing the result to adjacent ones, but it provides several improvements: We
address occlusions geometrically and differentiably, directly using the depth
maps as predicted during training. We introduce randomized layer normalization,
a novel powerful regularizer, and we account for object motion relative to the
scene. To the best of our knowledge, our work is the first to learn the camera
intrinsic parameters, including lens distortion, from video in an unsupervised
manner, thereby allowing us to extract accurate depth and motion from arbitrary
videos of unknown origin at scale. We evaluate our results on the Cityscapes,
KITTI and EuRoC datasets, establishing new state of the art on depth prediction
and odometry, and demonstrate qualitatively that depth prediction can be
learned from a collection of YouTube videos