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Found 19 papers, 4 papers with code
LPCM: Learning-based Predictive Coding for LiDAR Point Cloud Compression
The predictive geometry coding method in the geometry-based point cloud compression (G-PCC) standard uses the inherent angular resolution to predict the azimuth angles.
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds
We retain crucial information from the original data by extracting edge points and pixels, enhancing registration accuracy while maintaining computational efficiency.
PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport
Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques.
Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation
To address these challenges, we propose a feature extraction module, a feature refinement module, and a feature enhancement module.
CS-Net:Contribution-based Sampling Network for Point Cloud Simplification
Our network consists of a feature embedding module, a cascade attention module, and a contribution scoring module.
SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds
We propose an end-to-end attribute compression method for dense point clouds.
Feature Compression for Cloud-Edge Multimodal 3D Object Detection
Given a sparse tensor-based object detection network at the edge device, we introduce two modes to accommodate different application requirements: Transmission-Friendly Feature Compression (T-FFC) and Accuracy-Friendly Feature Compression (A-FFC).
3DAttGAN: A 3D Attention-based Generative Adversarial Network for Joint Space-Time Video Super-Resolution
The discriminative network uses a two-branch structure to handle details and motion information, making the generated results more accurate.
Generative Adversarial Network
Space-time Video Super-resolution
+1
Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss
In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label, and the cross-entropy between the one-hot encoding and the probability distribution predicted by the context model as the loss function.
Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction
The proposed module can predict the number of occupied child nodes and map it into an 8- dimensional vector to assist the context model in predicting the probability distribution of the occupancy of the current node for efficient entropy coding.
Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution
To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module.
PCAC-GAN: A Sparse-Tensor-Based Generative Adversarial Network for 3D Point Cloud Attribute Compression
Learning-based methods have proven successful in compressing geometric information for point clouds.
Relaxed forced choice improves performance of visual quality assessment methods
The experimental results show that the inclusion of the ``not sure'' response option in the forced choice method reduced mental load and led to models with better data fit and correspondence to ground truth.
GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute
To reduce color distortion in point clouds, we propose a graph-based quality enhancement network (GQE-Net) that uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently.
Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds
In this paper, a progressive knowledge transfer based on human visual perception mechanism for perceptual quality assessment of point clouds (PKT-PCQA) is proposed.
PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling
We propose a generative adversarial network for point cloud upsampling, which can not only make the upsampled points evenly distributed on the underlying surface but also efficiently generate clean high frequency regions.
Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression
In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate.
SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning
We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples.
Image-based Natural Language Understanding Using 2D Convolutional Neural Networks
We propose a new approach to natural language understanding in which we consider the input text as an image and apply 2D Convolutional Neural Networks to learn the local and global semantics of the sentences from the variations ofthe visual patterns of words.
