Integrating Multi-view Multi-light Surface Reconstruction into Cultural Heritage Workflows
本文解决了文化遗产工作流程中多视角多光照表面重建的问题，通过将先进的计算机视觉方法整合到开源摄影测量框架Meshroom中。
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ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction
本文提出了一种自适应网格优化框架和实用的网格渲染技术，通过结合可优化的Delaunay三角化四面体网格与多分辨率哈希网格，解决了高质量网格生成的问题。
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SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection
This study addresses key barriers to deploying clinical AI models—namely data heterogeneity, lack of deployment standardization, and stringent hospital requirements for high concurrency and low latency. The authors propose an open-source, modular platform integrating FHIR-inspired CDA preprocessing, NoSQL storage, containerized LightGBM model serving, and a Streamlit-based clinical dashboard, with scalable orchestration via Docker and Kubernetes. For the first time, they quantitatively demonstrate a U-shaped scaling behavior under clinical AI inference workloads: on a 12-thread CPU, increasing service replicas from 3 to 12 reduces p95 latency by 57.3% (from 3.3 s to 1.41 s) and eliminates request failures, whereas further scaling degrades performance due to scheduling contention. This work delivers an end-to-end, reproducible architecture for high-performance clinical AI deployment.
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Integrating Multi-view Multi-light Surface Reconstruction into Cultural Heritage Workflows
本文解决了文化遗产工作流程中多视角多光照表面重建的问题，通过将先进的计算机视觉方法整合到开源摄影测量框架Meshroom中。
0 citationsRead paper
ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction
本文提出了一种自适应网格优化框架和实用的网格渲染技术，通过结合可优化的Delaunay三角化四面体网格与多分辨率哈希网格，解决了高质量网格生成的问题。
0 citationsRead paper
SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection
This study addresses key barriers to deploying clinical AI models—namely data heterogeneity, lack of deployment standardization, and stringent hospital requirements for high concurrency and low latency. The authors propose an open-source, modular platform integrating FHIR-inspired CDA preprocessing, NoSQL storage, containerized LightGBM model serving, and a Streamlit-based clinical dashboard, with scalable orchestration via Docker and Kubernetes. For the first time, they quantitatively demonstrate a U-shaped scaling behavior under clinical AI inference workloads: on a 12-thread CPU, increasing service replicas from 3 to 12 reduces p95 latency by 57.3% (from 3.3 s to 1.41 s) and eliminates request failures, whereas further scaling degrades performance due to scheduling contention. This work delivers an end-to-end, reproducible architecture for high-performance clinical AI deployment.
0 citationsRead paper