Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding

Indoor scenes often contain complex layouts and interactions between objects, making 3D modeling of point clouds inherently difficult. In this paper, we design a divide-and-conquer modeling method considering the structural differences between indoor walls and internal objects. To achieve semantic understanding, we propose an effective 3D instance segmentation module using a deep network Indoor3DNet combined with super-point clustering, which provides a larger receptive field and maintains the continuity of individual objects. The Indoor3DNet includes an efficient point feature extraction backbone with good operability for different object granularity. In addition, we use a geometric primitives-based modeling approach to generate lightweight polygonal facets for walls and use a cross-modal registration technique to fit the corresponding instance models for internal objects based on their semantic labels. This modeling method can restore correct geometric shapes and topological relationships while maintaining a very lightweight structure. We have tested the method on diverse datasets, and the experimental results demonstrate that the method outperforms the state-of-the-art in terms of performance and robustness.