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Found 23 papers, 11 papers with code
OCMG-Net: Neural Oriented Normal Refinement for Unstructured Point Clouds
We present a robust refinement method for estimating oriented normals from unstructured point clouds.
Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis
This paper presents a novel non-rigid point set registration method that is inspired by unsupervised clustering analysis.
E$^3$-Net: Efficient E(3)-Equivariant Normal Estimation Network
To address this issue, we propose E3-Net to achieve equivariance for normal estimation.
TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis
Motivated by these insights, we introduce a Text-oriented Cross-Attention Network (TCAN), emphasizing the predominant role of the text modality in MSA.
Deep Learning-based Image and Video Inpainting: A Survey
The goal of this paper is to comprehensively review the deep learning-based methods for image and video inpainting.
SfmCAD: Unsupervised CAD Reconstruction by Learning Sketch-based Feature Modeling Operations
This paper introduces SfmCAD a novel unsupervised network that reconstructs 3D shapes by learning the Sketch-based Feature Modeling operations commonly used in modern CAD workflows.
CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-scale Geometry
This work presents an accurate and robust method for estimating normals from point clouds.
SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations
Reverse engineering CAD models from raw geometry is a classic but strenuous research problem.
DPE: Disentanglement of Pose and Expression for General Video Portrait Editing
In this paper, we introduce a novel self-supervised disentanglement framework to decouple pose and expression without 3DMMs and paired data, which consists of a motion editing module, a pose generator, and an expression generator.
Structure-Aware Surface Reconstruction via Primitive Assembly
We propose a novel and efficient method for reconstructing manifold surfaces from point clouds.
GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation
We propose a precise and efficient normal estimation method that can deal with noise and nonuniform density for unstructured 3D point clouds.
Ranked #4 on
Surface Normals Estimation
on PCPNet
SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds
Registering urban point clouds is a quite challenging task due to the large-scale, noise and data incompleteness of LiDAR scanning data.
Self-supervised Re-renderable Facial Albedo Reconstruction from Single Image
To further make facial textures disentangled with illumination, we propose a novel detailed illumination representation which is reconstructed with the detailed albedo together.
Robust Multi-view Registration of Point Sets with Laplacian Mixture Model
The proposed method assumes each data point is generated by a Laplacian Mixture Model (LMM), where its centers are determined by the corresponding points in other point sets.
Diffractive lensless imaging with optimized Voronoi-Fresnel phase
Here we report a diffractive lensless camera with spatially-coded Voronoi-Fresnel phase to achieve superior image quality.
Single-Image Specular Highlight Removal via Real-World Dataset Construction
Specular reflections pose great challenges on various multimedia and computer vision tasks, e. g. , image segmentation, detection and matching.
Text-Aware Single Image Specular Highlight Removal
The core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images.
LARNet: Lie Algebra Residual Network for Face Recognition
We prove that face rotation in the image space is equivalent to an additive residual component in the feature space of CNNs, which is determined solely by the rotation.
Scene text removal via cascaded text stroke detection and erasing
Specifically, we decouple the text removal problem into text stroke detection and stroke removal.
Pixel-wise Dense Detector for Image Inpainting
Recent GAN-based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar, which inevitably lose the position information of visual artifacts.
MGCN: Descriptor Learning using Multiscale GCNs
Second, we propose a new multiscale graph convolutional network (MGCN) to transform a non-learned feature to a more discriminative descriptor.
Detecting Colorized Images via Convolutional Neural Networks: Toward High Accuracy and Good Generalization
First, we design and implement a base network, which can attain better performance in terms of classification accuracy and generalization (in most cases) compared with state-of-the-art methods.
Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching
In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes.
