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

An internet reviews topic hierarchy mining method based on modified continuous renormalization procedure

Mining the hierarchical structure of Internet review topics and realizing a fine classification of review texts can help alleviate users' information overload. However, existing hierarchical topic classification methods primarily rely on external corpora and human intervention. This study proposes a Modified Continuous Renormalization (MCR) procedure that acts on the keyword co-occurrence network with fractal characteristics to achieve the topic hierarchy mining. First, the fractal characteristics in the keyword co-occurrence network of Internet review text are identified using a box-covering algorithm for the first time. Then, the MCR algorithm established on the edge adjacency entropy and the box distance is proposed to obtain the topic hierarchy in the keyword co-occurrence network. Verification data from the Dangdang.com book reviews shows that the MCR constructs topic hierarchies with greater coherence and independence than the HLDA and the Louvain algorithms. Finally, reliable review text classification is achieved using the MCR extended bottom level topic categories. The accuracy rate (P), recall rate (R) and F1 value of Internet review text classification obtained from the MCR-based topic hierarchy are significantly improved compared to four target text classification algorithms.Comment: 43 pages, 8 figures, conference or other essential inf

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

Linear Depth QFT over IBM Heavy-hex Architecture

Compiling a given quantum algorithm into a target hardware architecture is a challenging optimization problem. The compiler must take into consideration the coupling graph of physical qubits and the gate operation dependencies. The existing noise in hardware architectures requires the compilation to use as few running cycles as possible. Existing approaches include using SAT solver or heuristics to complete the mapping but these may cause the issue of either long compilation time (e.g., timeout after hours) or suboptimal compilation results in terms of running cycles (e.g., exponentially increasing number of total cycles). In this paper, we propose an efficient mapping approach for Quantum Fourier Transformation (QFT) circuits over the existing IBM heavy-hex architecture. Such proposal first of all turns the architecture into a structure consisting of a straight line with dangling qubits, and then do the mapping over this generated structure recursively. The calculation shows that there is a linear depth upper bound for the time complexity of these structures and for a special case where there is 1 dangling qubit in every 5 qubits, the time complexity is 5N+O(1). All these results are better than state of the art methods

SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models

The development of text-to-video (T2V), i.e., generating videos with a given text prompt, has been significantly advanced in recent years. However, relying solely on text prompts often results in ambiguous frame composition due to spatial uncertainty. The research community thus leverages the dense structure signals, e.g., per-frame depth/edge sequences, to enhance controllability, whose collection accordingly increases the burden of inference. In this work, we present SparseCtrl to enable flexible structure control with temporally sparse signals, requiring only one or a few inputs, as shown in Figure 1. It incorporates an additional condition encoder to process these sparse signals while leaving the pre-trained T2V model untouched. The proposed approach is compatible with various modalities, including sketches, depth maps, and RGB images, providing more practical control for video generation and promoting applications such as storyboarding, depth rendering, keyframe animation, and interpolation. Extensive experiments demonstrate the generalization of SparseCtrl on both original and personalized T2V generators. Codes and models will be publicly available at https://guoyww.github.io/projects/SparseCtrl .Comment: Project page: https://guoyww.github.io/projects/SparseCtr

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