In recent years, extensive research has focused on 3D natural scene generation, but the domain of 3D city generation has not received as much exploration.
The key success factor of the video deblurring methods is to compensate for the blurry pixels of the mid-frame with the sharp pixels of the adjacent video frames.
Finally, we use the matting module which takes the image, trimap and context features to estimate the alpha matte.
Ranked #6 on Image Matting on Composition-1K (using extra training data)
For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame.
A multi-scale context-aware fusion module is then introduced to adaptively select high-quality reconstructions for different parts from all coarse 3D volumes to obtain a fused 3D volume.
Ranked #3 on 3D Object Reconstruction on Data3D−R2N2
In particular, we devise two novel differentiable layers, named Gridding and Gridding Reverse, to convert between point clouds and 3D grids without losing structural information.
Ranked #3 on Point Cloud Completion on Completion3D
Inspired by this, we propose a novel method, named Mem3D, that explicitly constructs shape priors to supplement the missing information in the image.
Inferring the 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the training set to solve the problem.
To overcome the limitation of separate optical flow estimation, we propose a Spatio-Temporal Filter Adaptive Network (STFAN) for the alignment and deblurring in a unified framework.
Ranked #3 on Deblurring on DVD (using extra training data)
Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles.
Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e. g., table legs) from different coarse 3D volumes to obtain a fused 3D volume.
Ranked #4 on 3D Object Reconstruction on Data3D−R2N2
FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.