Abstract:
Depth prediction from single image is a challenging task due to the intra scale ambiguity and unavailability of
prior information. The prediction of an unambiguous depth from
single RGB image is very important aspect for computer vision
applications. In this paper, an end-to-end sparse-to-dense network
(S2DNet) is proposed for single image depth estimation (SIDE).
The proposed network processes single image along with the additional sparse depth samples for depth estimation. The additional
sparse depth sample are acquired either with a low-resolution
depth sensor or calculated by visual simultaneous localization and
mapping (SLAM) algorithms. In first stage, the proposed S2DNet
estimates coarse-level depth map using sparse-to-dense coarse network (S2DCNet). In second stage, the estimated coarse-level depth
map is concatenated with the input image and used as an input
to the sparse-to-dense fine network (S2DFNet) for fine-level depth
map estimation. The proposed S2DFNet comprises of attention
map architecture which helps to estimate the prominent depth
information. The quantitative and qualitative performance evaluation of the proposed network has been carried out using the
error metrics. We perform complete evaluation of S2DNet on four
publically available benchmark data setsi.e. NYU Depth-V2 indoor
dataset [1], KITTI odometry outdoor dataset [2], KITTI depth
completion test database [3] and SUN-RGB database [4]. Further,
we have extended the proposed S2DNet for image de-hazing. The
experimental analysis shows that the proposed S2DNet outperforms the existing state-of-the-art methods for both single image
depth estimation and image de-hazing.
