用完整物体先验生成缺失形状，提升物品重组精度。
Jigsaw++: Imagining Complete Shape Priors for Object Reassembly
- 学习完整物体先验，通过重定向策略融合已有组装结果。
- 在Breaking Bad和PartNet数据集上降低重建误差，提升形状精度。
- 适合需要高精度3D重建的逆向工程与文物修复场景。
自动组装问题因涉及复杂的3D表示而日益受到关注。本文提出Jigsaw++，一种新型生成方法，旨在应对物品重组中完整形状重建的多重挑战。现有方法主要依赖碎片化信息进行部件与断裂处组装，常忽略完整物体先验的整合。Jigsaw++通过学习完整物体先验，采用提出的“重定向”策略，有效利用任意现有组装方法的输出，生成完整的形状重建。该能力使其可与当前方法正交运行。在Breaking Bad数据集和PartNet上的大量实验表明，Jigsaw++显著降低重建误差，提升形状重建精度，为未来重组模型的发展开辟新方向。
原文摘要 · Abstract (English)
The automatic assembly problem has attracted increasing interest due to its complex challenges that involve 3D representation. This paper introduces Jigsaw++, a novel generative method designed to tackle the multifaceted challenges of reconstructing complete shape for the reassembly problem. Existing approach focusing primarily on piecewise information for both part and fracture assembly, often overlooking the integration of complete object prior. Jigsaw++ distinguishes itself by learning a shape prior of complete objects. It employs the proposed "retargeting" strategy that effectively leverages the output of any existing assembly method to generate complete shape reconstructions. This capability allows it to function orthogonally to the current methods. Through extensive evaluations on Breaking Bad dataset and PartNet, Jigsaw++ has demonstrated its effectiveness, reducing reconstruction errors and enhancing the precision of shape reconstruction, which sets a new direction for future reassembly model developments.
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