Search Results for author:
Found 14 papers, 11 papers with code
CityDreamer: Compositional Generative Model of Unbounded 3D Cities
3D city generation is a desirable yet challenging task, since humans are more sensitive to structural distortions in urban environments.
Ranked #1 on
Scene Generation
on OSM
Learning Geometric Transformation for Point Cloud Completion
It exploits the repetitive geometric structures in common 3D objects to recover the complete shapes, which contains three sub-networks: geometric patch network, structure transformation network, and detail refinement network.
Ranked #4 on
Point Cloud Completion
on ShapeNet
Spatio-Temporal Deformable Attention Network for Video Deblurring
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.
Long-Range Feature Propagating for Natural Image Matting
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)
Efficient Regional Memory Network for Video Object Segmentation
For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame.
Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
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
GRNet: Gridding Residual Network for Dense Point Cloud Completion
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
Single-View 3D Object Reconstruction from Shape Priors in Memory
Inspired by this, we propose a novel method, named Mem3D, that explicitly constructs shape priors to supplement the missing information in the image.
Toward 3D Object Reconstruction from Stereo Images
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.
Spatio-Temporal Filter Adaptive Network for Video Deblurring
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)
DAVANet: Stereo Deblurring with View Aggregation
Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles.
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
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
A survey of dimensionality reduction techniques based on random projection
Dimensionality reduction techniques play important roles in the analysis of big data.
Comparison among dimensionality reduction techniques based on Random Projection for cancer classification
FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.
