We present Large Inverse Rendering Model (LIRM), a transformer architecture that jointly reconstructs high-quality shape, materials, and radiance fields with view-dependent effects in less than a second. Our model builds upon the recent Large Reconstruction Models (LRMs) that achieve state-of-the-art sparse-view reconstruction quality. However, existing LRMs struggle to reconstruct unseen parts accurately and cannot recover glossy appearance or generate relightable 3D contents that can be consumed by standard Graphics engines. To address these limitations, we make three key technical contributions to build a more practical multi-view 3D reconstruction framework. First, we introduce an update model that allows us to progressively add more input views to improve our reconstruction. Second, we propose a hexa-plane neural SDF representation to better recover detailed textures, geometry and material parameters. Third, we develop a novel neural directional-embedding mechanism to handle view-dependent effects. Trained on a large-scale shape and material dataset with a tailored coarse-to-fine training scheme, our model achieves compelling results. It compares favorably to optimization-based dense-view inverse rendering methods in terms of geometry and relighting accuracy, while requiring only a fraction of the inference time.

We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM), the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed video of any dynamic scene. Feed-forward scene reconstruction has gained significant attention for its ability to rapidly create digital replicas of real-world environments. However, most existing models are limited to static scenes and fail to reconstruct the motion of moving objects. Developing a feed-forward model for dynamic scene reconstruction poses significant challenges, including the scarcity of training data and the need for appropriate 3D representations and training paradigms. To address these challenges, we introduce several key technical contributions: an enhanced large-scale synthetic dataset with ground-truth multi-view videos and dense 3D scene flow supervision; a per-pixel deformable 3D Gaussian representation that is easy to learn, supports high-quality dynamic view synthesis, and enables long-range 3D tracking; and a large transformer network that achieves real-time, generalizable dynamic scene reconstruction. Extensive qualitative and quantitative experiments demonstrate that DGS-LRM achieves dynamic scene reconstruction quality comparable to optimization-based methods, while significantly outperforming the state-of-the-art predictive dynamic reconstruction method on real-world examples. Its predicted physically grounded 3D deformation is accurate and can be readily adapted for long-range 3D tracking tasks, achieving performance on par with state-of-the-art monocular video 3D tracking methods.

Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.

Maintaining stylistic consistency is crucial for the cohesion and aesthetic appeal of images, a fundamental requirement in effective image editing and inpainting. However, existing methods primarily focus on the semantic control of generated content, often neglecting the critical task of preserving this consistency. In this work, we introduce the Neural Scene Designer (NSD), a novel framework that enables photo-realistic manipulation of user-specified scene regions while ensuring both semantic alignment with user intent and stylistic consistency with the surrounding environment. NSD leverages an advanced diffusion model, incorporating two parallel cross-attention mechanisms that separately process text and style information to achieve the dual objectives of semantic control and style consistency. To capture fine-grained style representations, we propose the Progressive Self-style Representational Learning (PSRL) module. This module is predicated on the intuitive premise that different regions within a single image share a consistent style, whereas regions from different images exhibit distinct styles. The PSRL module employs a style contrastive loss that encourages high similarity between representations from the same image while enforcing dissimilarity between those from different images. Furthermore, to address the lack of standardized evaluation protocols for this task, we establish a comprehensive benchmark. This benchmark includes competing algorithms, dedicated style-related metrics, and diverse datasets and settings to facilitate fair comparisons. Extensive experiments conducted on our benchmark demonstrate the effectiveness of the proposed framework.

Real-time monitoring and analysis of sensitive areas in high-speed railway (HSR) are crucial for ensuring the safe and smooth operation of high-speed trains. To address the problem of frequent missing and false alarms caused by anomaly data in HSR monitoring system, this study proposes an innovative network framework: Intelligent detection network of HSR anomaly monitoring data (HSRA-Net). The framework comprises of two modules: the data augmentation module and the anomaly detection module. The data augmentation module designs multiple alternative generative adversarial networks for sample augmentation. To achieve the end-to-end classification, the anomaly detection module improves the residual network by creating a deep residual shrinkage network with self-attention (DRSN-SA). An online monitoring system was installed and operated continuously for several years on a high-speed turnout of a continuous beam bridge to validate the proposed framework. The collected data includes displacement, stress, and temperature. The proposed framework has demonstrated excellent performance, generalizability, and deployability through sufficient model comparison. It can accurately and efficiently diagnose anomalies in the operation of the monitoring system. This study is of great significance for improving the anomaly detection task of the HSR monitoring system.

Crowd counting methods typically predict a density map as an intermediate representation of counting, and achieve good performance. However, due to the perspective phenomenon, there is a scale variation in real scenes, which causes the density map-based methods suffer from a severe scene generalization problem because only a limited number of scales are fitted in density map prediction and generation. To address this issue, we propose a novel vision transformer network, i.e., CrowdFormer, and a density kernels fusion framework for more accurate density map estimation and generation, respectively. Thereafter, we incorporate these two innovations into an adaptive learning system, which can take both the annotation dot map and original image as input, and jointly learns the density map estimator and generator within an end-to-end framework. The experimental results demonstrate that the proposed model achieves the state-of-the-art in the terms of MAE and MSE (e.g., it achieved a MAE of 67.1 and MSE of 301.6 on NWPU-Crowd dataset.), and confirm the effectiveness of the proposed two designs. The code is https://github.com/special-yang/Top_Down-CrowdCounting.

In this paper, we demonstrate a novel landmark photo search and browsing system: Agate, which ranks landmark image search results considering their relevance, diversity and quality. Agate learns from community photos the most interested aspects and related activities of a landmark, and generates adaptively a Table of Content (TOC) as a summary of the attractions to facilitate the user browsing. Image search results are thus re-ranked with the TOC so as to ensure a quick overview of the attractions of the landmarks. A novel non-parametric TOC generation and set-based ranking algorithm, MoM-DPM Sets, is proposed as the key technology of Agate. Experimental results based on human evaluation show the effectiveness of our model and users' preference for Agate.