AI 中文总结
研究针对单图像3D场景生成中分辨率受限问题，提出HIVE-3D方法。基于分层体素增强框架，经图像分割、注意力检索、构建分层组件树及体素超分辨率模型，实现粗到细的分层超分辨率，显著优于先前方法，达当前最优性能。
AI 中文摘要
最近，一系列作品能从单张图像生成令人印象深刻的3D对象，但受限于表示分辨率，不适用于3D场景生成。本文介绍HIVE-3D，一种基于分层体素增强框架的高质量3D场景生成新方法。输入单一场景图像，先生成粗糙初始场景，引入图像分割和基于注意力的检索使2D图像组件与3D场景组件对齐，组织场景关系成分层组件树，最后提出体素超分辨率模型生成精细体素。通过粗到细的分层超分辨率，生成高分辨率高质量3D场景，实验表明该方法显著优于先前方法。
英文摘要
Recently, a line of works can generate impressive 3D objects from a single image, but they are limited by restricted representation resolution, making them unsuitable for 3D scene generation. In this work, we introduce HIVE-3D, a novel method for high-quality 3D scene generation based on hierarchical voxel enhancement framework. Specifically, given a single scene image as input, we first produce a coarse initial scene, then introduce image segmentation and attention-based retrieval to align 2D image components with 3D scene components. Subsequently, we organize these scene relations into a hierarchical component tree, where nodes closer to the leaves denote finer-grained components. Finally, we propose a voxel super-resolution model that generates refined voxels for the target instance while maintaining strong consistency with the coarse voxels. Equipped with this model, we perform coarse-to-fine hierarchical super-resolution on images and voxels for each component, producing a high-resolution and high-quality 3D scene. Extensive experiments demonstrate that our method significantly outperforms previous approaches, achieving state-of-the-art performance.
CommentsAccepted at the 43rd International Conference on Machine Learning (ICML 2026). Project page: https://xbdff.github.io/HIVE-3D/
