论文标题
表面正常聚类用于曼哈顿场景的隐式表示
Surface Normal Clustering for Implicit Representation of Manhattan Scenes
论文作者
论文摘要
使用隐式神经场表示的新型视图合成和3D建模对于校准的多视图摄像机非常有效。众所周知，此类表示将受益于其他几何和语义监督。利用额外监督的大多数现有方法都需要密集的像素标签或局部场景先验。这些方法无法从场景描述中提供的高级模糊场景先验中受益。在这项工作中，我们旨在利用曼哈顿场景的几何事物来改善隐式神经辐射场表示。更确切地说，我们假设只有曼哈顿的室内场景（正在调查中）的知识 - 没有任何其他信息，没有任何其他信息。这样的高级先验用于自我观察在隐式神经场中明确衍生的表面正态。我们的建模使我们能够聚集派生的正常性，并利用其正交性约束以进行自我判断。我们在各种室内场景数据集上进行的详尽实验证明了该方法与已建立的基准相比。源代码可在https://github.com/nikola3794/normal-clustering-nerf上找到。
Novel view synthesis and 3D modeling using implicit neural field representation are shown to be very effective for calibrated multi-view cameras. Such representations are known to benefit from additional geometric and semantic supervision. Most existing methods that exploit additional supervision require dense pixel-wise labels or localized scene priors. These methods cannot benefit from high-level vague scene priors provided in terms of scenes' descriptions. In this work, we aim to leverage the geometric prior of Manhattan scenes to improve the implicit neural radiance field representations. More precisely, we assume that only the knowledge of the indoor scene (under investigation) being Manhattan is known -- with no additional information whatsoever -- with an unknown Manhattan coordinate frame. Such high-level prior is used to self-supervise the surface normals derived explicitly in the implicit neural fields. Our modeling allows us to cluster the derived normals and exploit their orthogonality constraints for self-supervision. Our exhaustive experiments on datasets of diverse indoor scenes demonstrate the significant benefit of the proposed method over the established baselines. The source code is available at https://github.com/nikola3794/normal-clustering-nerf.
