用3D生成先验实现零样本点云补全，无需训练数据
GenPC: Zero-shot Point Cloud Completion via 3D Generative Priors
- 通过深度图桥接单视图图像与3D生成模型，实现零样本补全
- 保留原始部分结构，自适应调整生成形状的位姿与尺度
- 在多个基准上表现优越，适合真实世界扫描补全场景
现有点云补全方法通常依赖预定义的合成训练数据，在处理分布外的真实扫描时面临挑战。为此，我们提出零样本补全框架GenPC，利用显式的3D生成先验重建高质量真实扫描。核心思想是：近期前馈式3D生成模型在大规模互联网数据上训练后，已具备从单视图图像进行零样本3D生成的能力。为将其用于补全，我们首先设计深度提示模块，通过深度图作为中介，将部分点云与图像到3D生成模型关联。为保留输入的原始部分结构，我们进一步提出几何保持融合模块，通过自适应调整生成形状的位姿和尺度实现对齐。在广泛使用的基准上的大量实验验证了方法的优越性和泛化能力，推动了鲁棒真实世界扫描补全的发展。
原文摘要 · Abstract (English)
Existing point cloud completion methods, which typically depend on predefined synthetic training datasets, encounter significant challenges when applied to out-of-distribution, real-world scans. To overcome this limitation, we introduce a zero-shot completion framework, termed GenPC, designed to reconstruct high-quality real-world scans by leveraging explicit 3D generative priors. Our key insight is that recent feed-forward 3D generative models, trained on extensive internet-scale data, have demonstrated the ability to perform 3D generation from single-view images in a zero-shot setting. To harness this for completion, we first develop a Depth Prompting module that links partial point clouds with image-to-3D generative models by leveraging depth images as a stepping stone. To retain the original partial structure in the final results, we design the Geometric Preserving Fusion module that aligns the generated shape with input by adaptively adjusting its pose and scale. Extensive experiments on widely used benchmarks validate the superiority and generalizability of our approach, bringing us a step closer to robust real-world scan completion.
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