Recently, neural implicit surfaces have become popular for multi-view reconstruction. To facilitate practical applications like scene editing and manipulation, some works extend the framework with semantic masks input for the object-compositional reconstruction rather than the holistic perspective. Though achieving plausible disentanglement, the performance drops significantly when processing the indoor scenes where objects are usually partially observed. We propose RICO to address this by regularizing the unobservable regions for indoor compositional reconstruction. Our key idea is to first regularize the smoothness of the occluded background, which then in turn guides the foreground object reconstruction in unobservable regions based on the object-background relationship. Particularly, we regularize the geometry smoothness of occluded background patches. With the improved background surface, the signed distance function and the reversedly rendered depth of objects can be optimized to bound them within the background range. Extensive experiments show our method outperforms other methods on synthetic and real-world indoor scenes and prove the effectiveness of proposed regularizations.
翻译:最近,神经隐含表面在多视图重建中变得很受欢迎。为了便利现场编辑和操控等实际应用,有些作品将框架扩展为用于物体组合重建的语义面具输入,而不是整体视角。虽然在处理通常部分观测到物体的室内场景时,性能明显脱落。我们建议RICO通过将不可观测区域正规化用于室内结构重建来解决这个问题。我们的关键想法是首先规范隐蔽背景的平滑性,然后根据天体-地表关系指导不可观测区域的地表物体重建。特别是,我们规范隐蔽背景补丁的几何光滑性。随着背景表面的改善,签字的距离功能和反向形成的物体深度可以优化,将它们绑在背景范围内。广泛的实验表明,我们的方法超越了合成和真实世界室内场的其他方法,并证明拟议规范的有效性。</s>