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：近年来，神经隐式表面在多视角重建领域得到广泛应用。为支持场景编辑与操作等实际应用，部分研究通过引入语义掩码输入，以物体组合重建视角替代全局重建框架。尽管实现了合理的解耦，但在处理物体通常被部分观测的室内场景时，性能显著下降。我们提出RICO方法，通过对不可观测区域进行正则化以解决室内组合重建问题。核心思路是：首先对遮挡背景的平滑性施加正则化，进而基于物体-背景关系引导不可观测区域的前景物体重建。具体而言，我们约束被遮挡背景块面的几何平滑性；基于优化后的背景表面，可对物体的符号距离函数及其反向渲染深度进行优化，使其限制在背景范围之内。大量实验表明，本方法在合成数据集与真实室内场景中均优于现有方法，并验证了所提正则化策略的有效性。