基于全局稀疏局部密集网格的快速单目场景重建
Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids
AI总结：
提出基于全局稀疏局部密集体素块网格直接使用SDF的无MLP单目场景重建方法，结合尺度校准与可微渲染，训练速度提升10倍、渲染速度提升100倍且精度相当。
摘要：
从单目图像进行室内场景重建一直备受增强现实和机器人开发者的青睐。神经场表示和单目先验的最新进展在场景级表面重建中取得了显著成果。然而，对多层感知机（MLP）的依赖极大限制了训练和渲染速度。在本工作中，我们提出在稀疏体素块网格中直接使用符号距离函数（SDF），以实现无MLP的快速准确场景重建。我们全局稀疏且局部密集的数据结构利用了表面的空间稀疏性，支持缓存友好的查询，并允许直接扩展至颜色和语义标签等多模态数据。为将此表示应用于单目场景重建，我们开发了一种尺度校准算法，用于从单目深度先验进行快速几何初始化。我们应用可微分体积渲染从此初始化中细化细节，实现快速收敛。我们还引入了高效的高维连续随机场（CRF），以进一步利用场景对象间的语义-几何一致性。实验表明，我们的方法训练速度提升10倍，渲染速度提升100倍，同时达到了与最先进神经隐式方法相当的精度。
英文摘要：
Indoor scene reconstruction from monocular images has long been sought after by augmented reality and robotics developers. Recent advances in neural field representations and monocular priors have led to remarkable results in scene-level surface reconstructions. The reliance on Multilayer Perceptrons (MLP), however, significantly limits speed in training and rendering. In this work, we propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction without MLPs. Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data such as color and semantic labels. To apply this representation to monocular scene reconstruction, we develop a scale calibration algorithm for fast geometric initialization from monocular depth priors. We apply differentiable volume rendering from this initialization to refine details with fast convergence. We also introduce efficient high-dimensional Continuous Random Fields (CRFs) to further exploit the semantic-geometry consistency between scene objects. Experiments show that our approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
