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Found 21 papers, 16 papers with code
CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities
Our main insights are 1) 4D city generation should separate dynamic objects (e. g., vehicles) from static scenes (e. g., buildings and roads), and 2) all objects in the 4D scene should be composed of different types of neural fields for buildings, vehicles, and background stuff.
DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes
2) A DiT-based diffusion model for HexPlane generation.
3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation.
2D Semantic-Guided Semantic Scene Completion
Semantic scene completion (SSC) aims to simultaneously perform scene completion (SC) and predict semantic categories of a 3D scene from a single depth and/or RGB image.
Ranked #1 on
3D Semantic Scene Completion
on NYUv2
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation
Crowd Motion Generation is essential in entertainment industries such as animation and games as well as in strategic fields like urban simulation and planning.
Ranked #1 on
Motion Generation
on KIL-ML
Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring
Specifically, BSSTNet (1) uses a longer temporal window in the transformer, leveraging information from more distant frames to restore the blurry pixels in the current frame.
Ranked #1 on
Deblurring
on DVD
GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation
Recently 3D Gaussian Splatting (3D-GS) has emerged as a highly efficient alternative for object-level 3D generation.
Ranked #1 on
Scene Generation
on KITTI
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 #2 on
Scene Generation
on GoogleEarth
(KID metric)
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.
Ranked #6 on
Deblurring
on DVD
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 #1 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.
