i.e., neural implicit representations that map a 3D coordinate to a representation of whatever is...
例如在医学中的CT、MRI,根据组织密度以及设定的水平集,就可以重构组织的三维体积模型。 1.2 什么是隐式神经表示 隐式神经表示(Implicit Neural Representation,INR)(有时也称为基于坐标的表示)是一种对各种信号进行参数化的新方法。传统的信号表示通常是离散的,而隐式神经表示将信号参数化为一个连续函数,将信号的域...
于是,可以考虑使用一个连续函数来表示图像的真实状态,然而我们无从得知这个连续函数的准确形式,因此有人提出用神经网络来逼近这个连续函数,这种表示方法被称为“隐式神经表示“ (Implicit Neural Representation,INR)。 举几个例子,图像、视频、体素,都能用INR来表示,其数学表达如下: 对于图像,INR函数将二维坐标映射到...
在位姿优化领域，BARF与ViewFormer分别从不同角度利用INR技术提升效率。BARF采用神经辐射场来优化相机位姿，而ViewFormer则利用Transformer解决仅基于少量图像的神经渲染问题。此外，Neural Sparse Voxel Fields与pixelNeRF也分别通过使用特征网格与像素级神经辐射场，进一步探索INR在SLAM应用的可能性。在三维场景重建...
Implicit representation steganographyRecently, implicit neural representation (INR) has started to be applied in image steganography. However, the quality of stego and secret images represented by INR is generally low. In this paper, we propose an implicit neural representation steganography method by ...
implicit neural representation代码解读在机器学习和深度学习中,隐式神经表示是一种非监督学习方法,用于学习数据的内在结构和模式。这种方法通常使用无监督学习算法来训练神经网络,以学习数据的低维表示。 以下是隐式神经表示的代码解读: ```python import torch import torch.nn as nn import torch.optim as optim #...
GIFS: Neural Implicit Function for General Shape Representation, CVPR 2022 computer-vision3d-shapeneural-implicit-representations UpdatedJul 13, 2024 Python xucao-42/mvas Star45 Code Issues Pull requests [CVPR 2023] Multi-View Azimuth Stereo via Tangent Space Consistency ...
【ICCV'2023🔥】Implicit Neural Representation for Cooperative Low-light Image Enhancement Welcome! This is the official implementation of our paper: Implicit Neural Representation for Cooperative Low-light Image Enhancement Authors: Shuzhou Yang, Moxuan Ding, Yanmin Wu, Zihan Li, Jian Zhang*. 📣 ...
Arbitrary scale super-resolution diffusion model for brain MRI images Implicit neural representationMagnetic resonance imaging? 2024 Elsevier LtdGiven the constraints posed by hardware capacity, scan duration, and patient cooperation... Z Han,W Huang - 《Computers in Biology & Medicine》 被引量: 0发...
Implicit neural representation (INR) has recently emerged as a promising paradigm for signal representations, which takes coordinates as inputs and generates corresponding signal values. Since these coordinates contain no semantic features, INR fails to take any semantic information into consideration. ...