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The paper introduces FurnSet, a framework for single-view 3D scene reconstruction that explicitly leverages repeated object instances to improve reconstruction quality. FurnSet uses per-object CLS tokens and a set-aware self-attention mechanism to group identical instances and aggregate observations across them for joint reconstruction. Experiments on 3D-Future and 3D-Front datasets demonstrate that FurnSet achieves improved scene reconstruction quality by exploiting repetition.
Reconstructing 3D scenes from a single view gets a boost by explicitly recognizing and leveraging repeated object instances, like chairs and tables, to inform and refine the reconstruction.
Single-view 3D scene reconstruction involves inferring both object geometry and spatial layout. Existing methods typically reconstruct objects independently or rely on implicit scene context, failing to exploit the repeated instances commonly present in realworld scenes. We propose FurnSet, a framework that explicitly identifies and leverages repeated object instances to improve reconstruction. Our method introduces per-object CLS tokens and a set-aware self-attention mechanism that groups identical instances and aggregates complementary observations across them, enabling joint reconstruction. We further combine scene-level and object-level conditioning to guide object reconstruction, followed by layout optimization using object point clouds with 3D and 2D projection losses for scene alignment. Experiments on 3D-Future and 3D-Front demonstrate improved scene reconstruction quality, highlighting the effectiveness of exploiting repetition for robust 3D scene reconstruction.
