Abstract:
Scene understanding is an active area of research in computer vision that encompasses several different problems.
The LiDARs and stereo depth sensor have their own restrictions such as light sensitiveness, power consumption
and short-range [1]. In this paper, we propose a two-stream
deep adversarial network for single image depth estimation
in RGB images. For stream I network, we propose a novel
encoder-decoder architecture using residual concepts to extract course-level depth features. Stream II network purely
processes the information through the residual architecture
for fine-level depth estimation. Also, we designed a feature
map sharing architecture to share the learned feature maps
of the decoder module of stream I. Sharing feature maps
strengthen the residual learning to estimate the scene depth
and increase the robustness of the proposed network. A
benchmark NYU RGB-D v2 [2] database is used to evaluate
the proposed network for single image depth estimation. Both
qualitative and quantitative analysis has been carried out to
analyze the effectiveness of the proposed network for scene
depth prediction. Performance analysis shows that the proposed method outperforms other existing methods for single
image depth estimation
