The generation of 3d content is essential in many applications and especially the ones relying on eXtended Reality (XR). However, creating XR-ready models is typically a costly and timely manual process, as well as a ...
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This study introduces a novel framework for estimating hurricane-induceddamage to individual buildings by integrating ai-based3d building modeling, Computational Fluiddynamics (CFd), and stratified sampling. By lev...
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This study introduces a novel framework for estimating hurricane-induceddamage to individual buildings by integrating ai-based3d building modeling, Computational Fluiddynamics (CFd), and stratified sampling. By leveraging the precise geometric characteristics of individual buildings extracted from digital data, the framework enables the accurate computation of pressure distributions for individual buildings through high-fidelity CFd simulations. The subsequent integration of an ai-based building component detection technique with vulnerability modeling and enhanced stratified sampling enables the rapid computation of the component and building-level failure probabilities. Applied to a case study in Atlantic City, NJ, the research underscores the effectiveness of realistic geometric characterizations and the inclusion of neighboring buildings in estimating individual-building level damage. Unlike conventional archetype-based approaches, the proposed methodology offers individual-building level risk assessment, reflecting the crucial role played by geometric and aerodynamic variables. The framework offers a promising step towards automated high-fidelity assessments of hurricane risks, leveraging detailedmodeling and simulation to contribute to more resilient communities.
As humans, we aspire to create media content that is both freely willed and readily controlled. Thanks to the prominent development of generative techniques, we now can easily utilize 2ddiffusion methods to synthesiz...
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ISBN:
(纸本)9798400705250
As humans, we aspire to create media content that is both freely willed and readily controlled. Thanks to the prominent development of generative techniques, we now can easily utilize 2ddiffusion methods to synthesize images controlled by raw sketch or designated human poses, and even progressively edit/regenerate local regions with masked inpainting. However, similar workflows in 3dmodeling tasks are still unavailable due to the lack of controllability and efficiency in 3d generation. In this paper, we present a novel controllable and interactive 3d assets modeling framework, named Coin3d. Coin3d allows users to control the 3d generation using a coarse geometry proxy assembled from basic shapes, and introduces an interactive generation workflow to support seamless local part editing while delivering responsive 3d object previewing within a few seconds. To this end, we develop several techniques, including the 3d adapter that applies volumetric coarse shape control to the diffusion model, proxy-bounded editing strategy for precise part editing, progressive volume cache to support responsive preview, and volume-SdS to ensure consistent mesh reconstruction. Extensive experiments of interactive generation and editing on diverse shape proxies demonstrate that our method achieves superior controllability and flexibility in the 3d assets generation task. Code anddata are available on the project webpage: https://***/coin3d/.
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