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Silo: Half-Gigapixel Cylindrical Stereoscopic Immersive Display 32

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Silo: Half-Gigapixel Cylindrical Stereoscopic Immersive Disp...

32nd IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Boorboor, Saeed Gutierrez-Rosales, Doris Shoaib, Ahamed Kalsi, Chahat Wang, Yue Cao, Yuyang Gu, Xianfeng Kaufman, Arie E. Stony Brook University Center for Visual Computing Stony BrookNY United States

ISBN: (纸本)9798331536459

We present the design and construction of the Silo, a fully immersive stereoscopic cylindrical tiled-display visualization facility. Comprising 168 high-density LCD displays, the facility provides an ultra-high-resolution image of 619 million pixels, and close to 360 horizontal field-of-regards (FoR), aiming to maximize visual acuity and completely engage the human visual sensorium and its periphery. In this article, we outline the motivations, design principles, hardware selection and software systems, and interaction modalities used in constructing the Silo. To address missing visual information due to the absence of a ceiling and floor, we have designed a method that utilizes conformal mapping and optimal mass transport to reproject the entire 360 volumetric FoR of the virtual scene to the available display real estate. We showcase several applications demonstrating the utility of the Silo and report the findings of our user studies that highlight the effectiveness of the Silo layout compared to curved mono and flat powerwall display facilities. Our user evaluations and studies have shown that the Silo supports natural exploration and enhanced visualization due to its capability to render surround ultra-high-resolution stereoscopic views. © 2025 IEEE.

关键词： Conformal mapping

NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity

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ACM Transactions on Graphics 2025年第2期44卷 1-18页

作者： Dave, Akshat Zhang, Tianyi Young, Aaron Raskar, Ramesh Heidrich, Wolfgang Veeraraghavan, Ashok Mit Media Lab Cambridge United States Rice University Houston United States Visual Computing Center King Abdullah University of Science and Technology Thuwal Saudi Arabia

Photoelasticity enables full-field stress analysis in transparent objects through stress-induced birefringence. Existing techniques are limited to two-dimensional (2D) slices and require destructively slicing the object. Recovering the internal three-dimensional (3D) stress distribution of the entire object is challenging, as it involves solving a tensor tomography problem and handling phase wrapping ambiguities. We introduce NeST, an analysis-by-synthesis approach for reconstructing 3D stress tensor fields as neural implicit representations from polarization measurements. Our key insight is to jointly handle phase unwrapping and tensor tomography using a differentiable forward model based on Jones calculus. Our non-linear model faithfully matches real captures, unlike prior linear approximations. We develop an experimental multi-axis polariscope setup to capture 3D photoelasticity and experimentally demonstrate that NeST reconstructs the internal stress distribution for objects with varying shape and force conditions. Additionally, we showcase novel applications in stress analysis, such as visualizing photoelastic fringes by virtually slicing the object and viewing photoelastic fringes from unseen viewpoints. NeST paves the way for scalable non-destructive 3D photoelastic analysis. © 2025 Copyright held by the owner/author(s).

关键词： 3D reconstruction

Introducing Unbiased Depth into 2D Gaussian Splatting for High-accuracy Surface Reconstruction

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arXiv 2025年

作者： Peng, Xiaoming Yang, Yixin Zhou, Yang Huang, Hui Visual Computing Research Center CCSE Shenzhen University China

Recently, 2D Gaussian Splatting (2DGS) has demonstrated superior geometry reconstruction quality than the popular 3DGS by using 2D surfels to approximate thin surfaces. However, it falls short when dealing with glossy surfacess, resulting in visible holes in these areas. We found the reflection discontinuity causes the issue. To fit the jump from diffuse to specular reflection at different viewing angles, depth bias is introduced in the optimized Gaussian primitives. To address that, we first replace the depth distortion loss in 2DGS with a novel depth convergence loss, which imposes a strong constraint on depth continuity. Then, we rectified the depth criterion in determining the actual surface, which fully accounts for all the intersecting Gaussians along the ray. Qualitative and quantitative evaluations across various datasets reveal that our method significantly improves reconstruction quality, with more complete and accurate surfaces than 2DGS. Copyright © 2025, The Authors. All rights reserved.

关键词： Surface reconstruction

Attention Distillation: A Unified Approach to visual Characteristics Transfer

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arXiv 2025年

作者： Zhou, Yang Gao, Xu Chen, Zichong Huang, Hui Visual Computing Research Center CSSE Shenzhen University China

Recent advances in generative diffusion models have shown a notable inherent understanding of image style and semantics. In this paper, we leverage the self-attention features from pretrained diffusion networks to transfer the visual characteristics from a reference to generated images. Unlike previous work that uses these features as plug-and-play attributes, we propose a novel attention distillation loss calculated between the ideal and current stylization results, based on which we optimize the synthesized image via backpropagation in latent space. Next, we propose an improved Classifier Guidance that integrates attention distillation loss into the denoising sampling process, further accelerating the synthesis and enabling a broad range of image generation applications. Extensive experiments have demonstrated the extraordinary performance of our approach in transferring the examples’ style, appearance, and texture to new images in synthesis. Code is available at https://***/xugao97/ AttentionDistillation. Copyright © 2025, The Authors. All rights reserved.

关键词： Image denoising

Silo: Half-Gigapixel Cylindrical Stereoscopic Immersive Display

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Silo: Half-Gigapixel Cylindrical Stereoscopic Immersive Disp...

IEEE Annual International Symposium Virtual Reality

作者： Saeed Boorboor Doris Gutiérrez-Rosales Ahamed Shoaib Chahat Kalsi Yue Wang Yuyang Cao Xianfeng Gu Arie E. Kaufman Center for Visual Computing Stony Brook University Stony Brook NY

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

关键词： visualization Three-dimensional displays Stereo image processing Data visualization Transportation Virtual reality Software systems Liquid crystal displays Floors Next generation networking

Explainable XR: Understanding User Behaviors of XR Environments using LLM-assisted Analytics Framework

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arXiv 2025年

作者： Kim, Yoonsang Aamir, Zainab Singh, Mithilesh Boorboor, Saeed Mueller, Klaus Kaufman, Arie E. Center for Visual Computing Stony Brook University New York United States

We present Explainable XR, an end-to-end framework for analyzing user behavior in diverse eXtended Reality (XR) environments by leveraging Large Language Models (LLMs) for data interpretation assistance. Existing XR user analytics frameworks face challenges in handling cross-virtuality – AR, VR, MR – transitions, multi-user collaborative application scenarios, and the complexity of multimodal data. Explainable XR addresses these challenges by providing a virtuality-agnostic solution for the collection, analysis, and visualization of immersive sessions. We propose three main components in our framework: (1) A novel user data recording schema, called User Action Descriptor (UAD), that can capture the users’ multimodal actions, along with their intents and the contexts;(2) a platform-agnostic XR session recorder, and (3) a visual analytics interface that offers LLM-assisted insights tailored to the analysts’ perspectives, facilitating the exploration and analysis of the recorded XR session data. We demonstrate the versatility of Explainable XR by demonstrating five use-case scenarios, in both individual and collaborative XR applications across virtualities. Our technical evaluation and user studies show that Explainable XR provides a highly usable analytics solution for understanding user actions and delivering multifaceted, actionable insights into user behaviors in immersive environments. Copyright © 2025, The Authors. All rights reserved.

关键词： visual analytics

Retrieval Augmented Recipe Generation

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Retrieval Augmented Recipe Generation

2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Liu, Guoshan Yin, Hailong Zhu, Bin Chen, Jingjing Ngo, Chong-Wah Jiang, Yu-Gang School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore Management University Singapore

ISBN: (纸本)9798331510831

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method - first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu-cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset. © 2025 IEEE.

关键词： Food ingredients

Making Large Language Models Better Reasoners with Orchestrated Streaming Experiences

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arXiv 2025年

作者： Liu, Xiangyang He, Junliang Qiu, Xipeng School of Computer Science Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

Large language models (LLMs) can perform complex reasoning by generating intermediate thoughts under zero-shot or few-shot settings. However, zero-shot prompting always encounters low performance, and the superior performance of few-shot prompting hinges on the manual-crafted demonstrations. In this paper, we present RoSE (Reasoning with Orchestrated Streaming Experiences), a general framework for solving reasoning tasks that can self-improve without complex external efforts. To enable RoSE, we describe an architecture that extends an LLM to store all answered questions and their thoughts in a streaming experience pool then orchestrates helpful questions from the pool to assist in answering new questions. To set up a question-aware orchestration mechanism, RoSE first calculates the similarity of each question in the pool with a new test question. Since the solution to each answered question is not always correct, RoSE will sort the questions according to their similarity with the new question, and then uniformly divide them into multiple buckets. It finally extracts one question from each bucket to make these extracted questions more diverse. To make these extracted questions help RoSE answer new questions as much as possible, we introduce two other attributes of uncertainty and complexity for each question. RoSE will preferentially select the questions with low uncertainty and high complexity from each bucket. We evaluate the versatility of RoSE in various reasoning tasks, LLMs, and CoT methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Complex networks

VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

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VIPNet: Combining Viewpoint Information and Shape Priors fo...

17th Asian Conference on Computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling