AnchorComplete: Recovering Complete Scene Meshes from Partial Point Clouds

Most image-based 3D reconstructors prioritize reconstructing visible surfaces, producing partial point clouds with large holes and missing areas due to limited field of view and occlusions. To address this gap, we introduce AnchorComplete, a framework that recovers complete scene meshes from partial point reconstructions. Our design addresses two key challenges: anchoring generation to the observed geometry and scaling completion to diverse scene layouts. We introduce a partial-point conditioning module that guides both sparse-structure and sparse-latent generation using the input point cloud, together with Swin-DiT backbones that replace full self-attention with 3D windowed attention for efficient high-resolution generation. We instantiate these designs on top of the sparse latent representation of TRELLIS. The resulting model produces complete scene meshes that remain faithful to the observed geometry, providing a more complete digital representation of the physical environment. Experiments on Replica and SCRREAM show that AnchorComplete consistently outperforms strong baselines in complete scene reconstruction, particularly under sparse-view settings.