《Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis Angela》-优快云博客

本文链接：https://blog.youkuaiyun.com/Bamboozq/article/details/89324218

本文介绍了一种名为3D-EPN的创新方法，该方法利用形状分类网络的语义上下文来完成部分扫描的3D模型。通过使用3D网格合成过程，该方法能生成高分辨率输出并保留局部几何细节，实现了端到端的模型补全。3D-EPN克服了传统手工设计和基于强数据库先验的方法的局限性，采用机器学习技术进行完全数据驱动的预测。

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pipeline

graph TD
A[3D-EPN: predict global structure in unknown area] --> B[correlate these intermediary result with 3D geometry from a shape database]
B --> C[patch-based 3D shape synthesis method]

Contribution

3D-EPN completes partially-scanned 3D models while using semantic context from a shape classification network.
3D mesh synthesis procedure to obtain high-resolution output and local geometry details
end to end completion method

shape completion

1、clean up for broken 3D models
laplacion smooth and poission reconstrcutin is in
the category

2、detecting structure and regularities in 3D shape
limits the shape space in hand-crafted design

3、Much research leverages strong data-base priors
limits cannot generalize to new shapes
there key insight is take these information for global structure rather than local information

4、fully data-driven method trained with machine learning techniques is promising direction

Voxlets

They train a random decision forests that predict unknown voxel neighborhoods; the final mesh is generated with a weighted average of the predicted results

3D ShapeNet

They also use convolutional neural networks – specifically a deep belief network – to obtain a generative model for a given shape database.
this strategy is significantly less efficient than directly training an end-to-end predictor as our 3D-EPN does