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Found 11 papers, 5 papers with code
Quality evaluation of point clouds: a novel no-reference approach using transformer-based architecture
With the increased interest in immersive experiences, point cloud came to birth and was widely adopted as the first choice to represent 3D media.
PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment
Following the advent of immersive technologies and the increasing interest in representing interactive geometrical format, 3D Point Clouds (PC) have emerged as a promising solution and effective means to display 3D visual information.
G-SemTMO: Tone Mapping with a Trainable Semantic Graph
In this work, we draw inspiration from an expert photographer's approach and present a Graph-based Semantic-aware Tone Mapping Operator, G-SemTMO.
A deep perceptual metric for 3D point clouds
In addition, we propose a novel truncated distance field voxel grid representation and find that it leads to sparser latent spaces and loss functions that are more correlated with perceived visual quality compared to a binary representation.
Improved Deep Point Cloud Geometry Compression
Point clouds have been recognized as a crucial data structure for 3D content and are essential in a number of applications such as virtual and mixed reality, autonomous driving, cultural heritage, etc.
Folding-based compression of point cloud attributes
However, as this mapping process is lossy in nature, we propose several strategies to refine it so that attributes can be mapped to the 2D grid with minimal distortion.
Generating Relevant Counter-Examples from a Positive Unlabeled Dataset for Image Classification
In this manner, the discriminator is constrained to request the generator to converge towards the unlabeled samples distribution while diverging from the positive samples distribution.
Self-supervised learning for autonomous vehicles perception: A conciliation between analytical and learning methods
Nowadays, supervised deep learning techniques yield the best state-of-the-art prediction performances for a wide variety of computer vision tasks.
Deep Tone Mapping Operator for High Dynamic Range Images
In this paper, we address this problem by proposing a fast, parameter-free and scene-adaptable deep tone mapping operator (DeepTMO) that yields a high-resolution and high-subjective quality tone mapped output.
Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of millions.
Learning Local Distortion Visibility From Image Quality Data-sets
In this paper, we explore a different perspective, and we investigate whether it is possible to learn local distortion visibility from image quality scores.
