Neural 3D Scene Reconstruction from Multiple 2D Images without 3D Supervision

Neural 3D scene reconstruction methods have achieved impressive performance when reconstructing complex geometry and low-textured regions in indoor scenes. However, these methods heavily rely on 3D data which is costly and time-consuming to obtain in real world. In this paper, we propose a novel neural reconstruction method that reconstructs scenes using sparse depth under the plane constraints without 3D supervision. We introduce a signed distance function field, a color field, and a probability field to represent a scene. We optimize these fields to reconstruct the scene by using differentiable ray marching with accessible 2D images as supervision. We improve the reconstruction quality of complex geometry scene regions with sparse depth obtained by using the geometric constraints. The geometric constraints project 3D points on the surface to similar-looking regions with similar features in different 2D images. We impose the plane constraints to make large planes parallel or vertical to the indoor floor. Both two constraints help reconstruct accurate and smooth geometry structures of the scene. Without 3D supervision, our method achieves competitive performance compared with existing methods that use 3D supervision on the ScanNet dataset.Comment: 10 pages, 6 figure

catena-Poly[[[aqua­[2-(6-chloro­pyridin-3-yl)acetato-κO]sodium]-di-μ-aqua] monohydrate]

The crystal structure of the title compound, {[Na(C7H5ClNO2)(H2O)3]·H2O}n, features polymeric chains along [010]. The Na+ cation is octa­hedrally coordinated by four bridging water mol­ecules, a terminal water mol­ecule and an O atom derived from a monodentate carboxyl­ate ligand. Adjacent polyhedra share two O⋯O edges. The polymeric chains are linked into a three-dimensional network via O—H⋯O and O—H⋯N hydrogen bonds

A Conceptual Artificial Intelligence Application Framework in Human Resource Management

This study proposes a conceptional framework of artificial intelligence (AI) technology application for human resource management (HRM). Based on the theory of the six basic dimensions of human resource management, which includes human resource strategy and planning, recruitment, training and development process, performance management, salary evaluation, and the employee relationship management, is combine with its potential corresponding AI technology application. With the cases analysis on recruitment of leap.ai and online training of Baidu, the recruitment dimension and training dimension with AI are further explored. Finally, the practical implication and future study are supplemented. This AIHRM conceptual model provides suggestions and directions for the development of AI in enterprise human resource management

Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition

The issue of single sample per person (SSPP) face recognition has attracted more and more attention in recent years. Patch/local-based algorithm is one of the most popular categories to address the issue, as patch/local features are robust to face image variations. However, the global discriminative information is ignored in patch/local-based algorithm, which is crucial to recognize the nondiscriminative region of face images. To make the best of the advantage of both local information and global information, a novel two-layer local-to-global feature learning framework is proposed to address SSPP face recognition. In the first layer, the objective-oriented local features are learned by a patch-based fuzzy rough set feature selection strategy. The obtained local features are not only robust to the image variations, but also usable to preserve the discrimination ability of original patches. Global structural information is extracted from local features by a sparse autoencoder in the second layer, which reduces the negative effect of nondiscriminative regions. Besides, the proposed framework is a shallow network, which avoids the over-fitting caused by using multilayer network to address SSPP problem. The experimental results have shown that the proposed local-to-global feature learning framework can achieve superior performance than other state-of-the-art feature learning algorithms for SSPP face recognition

Fuzzy superpixels for polarimetric SAR images classification

Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, no superpixel algorithm is especially designed for image classification. It is believed that both mixed superpixels and pure superpixels exist in an image.Nevertheless, mixed superpixels have negative effects on classification accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classification. In this paper, first, a novel superpixels concept, named fuzzy superpixels, is proposed for reducing the generation of mixed superpixels.In fuzzy superpixels ,not al lpixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assigning them to improper superpixels. Second,a new algorithm, named FuzzyS(FS),is proposed to generate fuzzy superpixels for PolSAR image classification. Three PolSAR images are used to verify the effect of the proposed FS algorithm. Experimental results demonstrate the superiority of the proposed FS algorithm over several state-of-the-art superpixels algorithms

Fuzzy Superpixels based Semi-supervised Similarity-constrained CNN for PolSAR Image Classification

Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels

Gastrointestinal Bioaccessibility and Colonic Fermentation of Fucoxanthin from the Extract of the Microalga Nitzschia laevis

The extract of microalga Nitzschia laevis (NLE) is considered a source of dietary fucoxanthin, a carotenoid possessing a variety of health benefits. In the present study, the bioaccessibility and deacetylation of fucoxanthin were studied by simulated in vitro gastrointestinal digestion and colonic batch fermentation. In the gastric phase, higher fucoxanthin loss was observed at pH 3 compared to pH 4 and 5. Lipases are crucial for the deacetylation of fucoxanthin into fucoxanthinol. Fucoxanthinol production decreased significantly in the order: pure fucoxanthin (25.3%) > NLE (21.3%) > fucoxanthin-containing emulsion (11.74%). More than 32.7% of fucoxanthin and fucoxanthinol was bioaccessible after gastrointestinal digestion of NLE. During colon fermentation of NLE, a higher loss of fucoxanthin and changes of short-chain fatty acid production were observed but no fucoxanthinol was detected. Altogether, we provided novel insights on the fucoxanthin fate along the human digestion tract and showed the potential of NLE as a promising source of fucoxanthin.</p

Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production

Amateurs working on mini-films and short-form videos usually spend lots of time and effort on the multi-round complicated process of setting and adjusting scenes, plots, and cameras to deliver satisfying video shots. We present Virtual Dynamic Storyboard (VDS) to allow users storyboarding shots in virtual environments, where the filming staff can easily test the settings of shots before the actual filming. VDS runs on a "propose-simulate-discriminate" mode: Given a formatted story script and a camera script as input, it generates several character animation and camera movement proposals following predefined story and cinematic rules to allow an off-the-shelf simulation engine to render videos. To pick up the top-quality dynamic storyboard from the candidates, we equip it with a shot ranking discriminator based on shot quality criteria learned from professional manual-created data. VDS is comprehensively validated via extensive experiments and user studies, demonstrating its efficiency, effectiveness, and great potential in assisting amateur video production.Comment: Project page: https://virtualfilmstudio.github.io

HanoiT: Enhancing Context-aware Translation via Selective Context

Context-aware neural machine translation aims to use the document-level context to improve translation quality. However, not all words in the context are helpful. The irrelevant or trivial words may bring some noise and distract the model from learning the relationship between the current sentence and the auxiliary context. To mitigate this problem, we propose a novel end-to-end encoder-decoder model with a layer-wise selection mechanism to sift and refine the long document context. To verify the effectiveness of our method, extensive experiments and extra quantitative analysis are conducted on four document-level machine translation benchmarks. The experimental results demonstrate that our model significantly outperforms previous models on all datasets via the soft selection mechanism