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Semantic scene reconstruction from sparse and incomplete point clouds is a critical task for point scene understanding. It aims to recognize semantic labels for objects and recover their complete shapes as meshes. Existing methods often fail to realize high-quality instance reconstruction due to inadequate shape representation and underutilization of proposal point clouds. To address these issues, we optimize the previous BSP/occupancy-to-mesh reconstruction framework to points-to-mesh and accomplish multi-level utilization of proposals. We chose point cloud as the representation of completion to reduce the difficulty of restoring curved shallow parts. Benefiting from the optimization, we can match and merge proposal point clouds with the restored ones, avoiding missing parts existing in inputs. We design an effective pose normalization module to extract point-based features from normalized proposals, which are fused with features extracted from voxelized proposals, avoiding the detailed geometry lost in voxelization and enhancing the reconstruction's robustness to different input postures. The suitable points-to-mesh reconstruction framework and full utilization of proposals make our method improve reconstruction results efficiently. Detailed experiments on the challenging ScanNet dataset of the semantic scene reconstruction benchmark show that our network outperforms state-of-the-art methods in both completion and mapping metrics.
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