Deep Convolutional Grid Warping Network for Joint Depth Map Upsampling
نویسندگان
چکیده
منابع مشابه
Joint convolutional neural pyramid for depth map super-resolution
High-resolution depth map can be inferred from a lowresolution one with the guidance of an additional highresolution texture map of the same scene. Recently, deep neural networks with large receptive fields are shown to benefit applications such as image completion. Our insight is that super resolution is similar to image completion, where only parts of the depth values are precisely known. In ...
متن کاملMulti-step joint bilateral depth upsampling
Depth maps are used in many applications, e.g. 3D television, stereo matching, segmentation, etc. Often, depth maps are available at a lower resolution compared to the corresponding image data. For these applications, depth maps must be upsampled to the image resolution. Recently, joint bilateral filters are proposed to upsample depth maps in a single step. In this solution, a high-resolution o...
متن کاملFast Depth Map Upsampling using Edge Information
In this paper, we propose a new depth map upsampling method to increase the depth image resolution using edge information. Although the joint bilateral upsampling (JBU) method expands the resolution of the depth map using two weighting functions, the complexity of JBU is relatively high. In the proposed upsampling method, we reduce the complexity of depth map upsmapling operation using a color ...
متن کاملJoint upsampling and noise reduction for real-time depth map enhancement
An efficient system that upsamples depth map captured by Microsoft Kinect while jointly reducing the effect of noise is presented. The upsampling is carried by detecting and exploiting the piecewise locally planar structures of the downsampled depth map, based on corresponding high-resolution RGB image. The amount of noise is reduced by accumulating the downsampled data simultaneously. By benef...
متن کاملDeep Depth Super-Resolution: Learning Depth Super-Resolution Using Deep Convolutional Neural Network
Depth image super-resolution is an extremely challenging task due to the information loss in sub-sampling. Deep convolutional neural network have been widely applied to color image super-resolution. Quite surprisingly, this success has not been matched to depth super-resolution. This is mainly due to the inherent difference between color and depth images. In this paper, we bridge up the gap and...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3015209