PartMosaic3D: COUPLING PART-AWARE DECOMPOSITION AND JOINT CONSTITUENT SELECTION FOR 3D OBJECT MOSAICS

We study 3D object mosaic synthesis from a fixed gallery: reconstructing a textured target mesh by placing whole objects retrieved from a gallery of 3D assets. Any solution makes two coupled decisions—how the target is partitioned into regions, and which object occupies each region—that existing approaches make implicitly or greedily. In this work, we present PARTMOSAIC3D, a formulation that makes both decisions explicit: regions are nodes of a learned semantic part hierarchy, refined where retrieval under a frozen shape representation is poor, and constituents are chosen jointly over the region adjacency graph with fidelity, fit, reuse and interpenetration costs. The fidelity-and-reuse core of this problem is an exactly solvable min-cost flow that contains the baseline’s greedy reuse heuristic, and the full problem is solved to certified optimality within candidate pools. A decomposition × selection factorial with matched-granularity controls tests whether and when each decision matters. On four targets, parts with joint selection raise coverage from 0.527–0.703 to 0.725–0.918 over voxels with greedy selection at matched granularity, and at the baseline’s granularity parts reduce the protrusion error from 1.71% to 0.30% and from 1.38% to 0.32% where parts are contiguous. Joint selection helps through its fit and overlap terms, cutting interpenetration volume by 30–54% on three targets, whereas exact optimization of fidelity and reuse alone changes the mosaics only marginally; retrievability-driven refinement showed no benefit over size-driven subdivision at the thresholds tested, and amodal completion did not help.