63 research outputs found
An Improved Neural Network Model Based On CNN Using For Fruit Sugar Degree Detection
Artificial Intelligence(AI) widely applies in Image Classification and
Recognition, Text Understanding and Natural Language Processing, which makes
great progress. In this paper, we introduced AI into the fruit quality
detection field. We designed a fruit sugar degree regression model using an
Artificial Neural Network based on spectra of fruits within the
visible/near-infrared(V/NIR)range. After analysis of fruit spectra, we
innovatively proposed a new neural network structure: low layers consist of a
Multilayer Perceptron(MLP), a middle layer is a 2-dimensional correlation
matrix layer, and high layers consist of several Convolutional Neural
Network(CNN) layers. In this study, we used fruit sugar value as a detection
target, collecting two fruits called Gan Nan Navel and Tian Shan Pear as
samples, doing experiments respectively, and comparing their results. We used
Analysis of Variance(ANOVA) to evaluate the reliability of the dataset we
collected. Then, we tried multiple strategies to process spectrum data,
evaluating their effects. In this paper, we tried to add Wavelet
Decomposition(WD) to reduce feature dimensions and a Genetic Algorithm(GA) to
find excellent features. Then, we compared Neural Network models with
traditional Partial Least Squares(PLS) based models. We also compared the
neural network structure we designed(MLP-CNN) with other traditional neural
network structures. In this paper, we proposed a new evaluation standard
derived from dataset standard deviation(STD) for evaluating detection
performance, validating the viability of using an artificial neural network
model to do fruit sugar degree nondestructive detection
LumiGAN: Unconditional Generation of Relightable 3D Human Faces
Unsupervised learning of 3D human faces from unstructured 2D image data is an
active research area. While recent works have achieved an impressive level of
photorealism, they commonly lack control of lighting, which prevents the
generated assets from being deployed in novel environments. To this end, we
introduce LumiGAN, an unconditional Generative Adversarial Network (GAN) for 3D
human faces with a physically based lighting module that enables relighting
under novel illumination at inference time. Unlike prior work, LumiGAN can
create realistic shadow effects using an efficient visibility formulation that
is learned in a self-supervised manner. LumiGAN generates plausible physical
properties for relightable faces, including surface normals, diffuse albedo,
and specular tint without any ground truth data. In addition to relightability,
we demonstrate significantly improved geometry generation compared to
state-of-the-art non-relightable 3D GANs and notably better photorealism than
existing relightable GANs.Comment: Project page: https://boyangdeng.com/projects/lumiga
NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
We address the problem of recovering the shape and spatially-varying
reflectance of an object from multi-view images (and their camera poses) of an
object illuminated by one unknown lighting condition. This enables the
rendering of novel views of the object under arbitrary environment lighting and
editing of the object's material properties. The key to our approach, which we
call Neural Radiance Factorization (NeRFactor), is to distill the volumetric
geometry of a Neural Radiance Field (NeRF) [Mildenhall et al. 2020]
representation of the object into a surface representation and then jointly
refine the geometry while solving for the spatially-varying reflectance and
environment lighting. Specifically, NeRFactor recovers 3D neural fields of
surface normals, light visibility, albedo, and Bidirectional Reflectance
Distribution Functions (BRDFs) without any supervision, using only a
re-rendering loss, simple smoothness priors, and a data-driven BRDF prior
learned from real-world BRDF measurements. By explicitly modeling light
visibility, NeRFactor is able to separate shadows from albedo and synthesize
realistic soft or hard shadows under arbitrary lighting conditions. NeRFactor
is able to recover convincing 3D models for free-viewpoint relighting in this
challenging and underconstrained capture setup for both synthetic and real
scenes. Qualitative and quantitative experiments show that NeRFactor
outperforms classic and deep learning-based state of the art across various
tasks. Our videos, code, and data are available at
people.csail.mit.edu/xiuming/projects/nerfactor/.Comment: Camera-ready version for SIGGRAPH Asia 2021. Project Page:
https://people.csail.mit.edu/xiuming/projects/nerfactor
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