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. The code is available at https://github.com/kyleleey/RICO.

翻译：近年来，神经隐式表面在多视图重建中得到了广泛应用。为赋能场景编辑与操控等实际应用，部分研究通过引入语义掩码输入，使框架能够实现面向对象的组合重建，而非仅进行全局视角重建。尽管该方法实现了有前景的解耦效果，但在处理物体通常被部分观测到的室内场景时，性能显著下降。我们提出RICO，通过正则化不可见区域来解决室内组合重建问题。其核心思想是，首先对遮挡背景的平滑性进行正则化，进而基于物体-背景关系，引导前景物体在不可见区域的重建。具体而言，我们正则化遮挡背景区域的几何平滑性。通过优化后的背景表面，可以对物体的符号距离函数和反向渲染深度进行优化，使其约束在背景范围内。大量实验表明，我们的方法在合成和真实室内场景中均优于现有方法，并验证了所提正则化的有效性。代码已开源：https://github.com/kyleleey/RICO。