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Neural Parametric Gaussians для реконструкции монокулярных нежестких объектов

Новый двухэтапный подход для реконструкции 3д объектов из видео, решая проблему ограниченного качества новых видов при значительном отличии от обучающих данных. На первом этапе создается грубая нейронная модель деформации с низкоранговой декомпозицией, обеспечивающая временную согласованность, а на втором этапе оптимизируются трехмерные гауссовы функции в локальных объемах для высококачественной фотореалистичной реконструкции. NPG демонстрирует превосходство над существующими методами, особенно в сложных сценариях с минимальными многоракурсными данными, благодаря сильной регуляризации и эффективному моделированию деформаций. Ограничения метода связаны с высокой сложностью задачи, включая влияние статичной камеры или сложных движений, что может приводить к деформациям шаблона.

https://arxiv.org/html/2312.01196v2

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#unrealneural
Neural Parametric Gaussians для реконструкции монокулярных нежестких объектов

Новый двухэтапный подход для реконструкции 3д объектов из видео, решая проблему ограниченного качества новых видов при значительном отличии от обучающих данных. На первом этапе создается грубая нейронная модель деформации с низкоранговой декомпозицией, обеспечивающая временную согласованность, а на втором этапе оптимизируются трехмерные гауссовы функции в локальных объемах для высококачественной фотореалистичной реконструкции. NPG демонстрирует превосходство над существующими методами, особенно в сложных сценариях с минимальными многоракурсными данными, благодаря сильной регуляризации и эффективному моделированию деформаций. Ограничения метода связаны с высокой сложностью задачи, включая влияние статичной камеры или сложных движений, что может приводить к деформациям шаблона.

https://arxiv.org/html/2312.01196v2
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