DeepSDF : Learning Continuous Signed Distance Functions for Shape Representation
计算机图形学，3D计算机视觉和机器人技术社区已经提出了多种方法来表示3D几何图形以进行渲染和重构。 这些提供了在保真度，效率和压缩能力之间的权衡。 在这项工作中，我们介绍了DeepSDF，这是一种学习到的连续形状的符号距离函数（SDF）表示形式，可以实现高质量的形状表示，部分和嘈杂的3D输入数据的内插和完成。 DeepSDF和它的经典对应物一样，通过连续的体积场表示形状的表面：该点的大小表示到表
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相关文章
- 1. Kernel Functions for Machine Learning Applications
- 2. Momentum Contrast for Unsupervised Visual Representation Learning
- 3. Self-Supervised Representation Learning
- 4. Predictive learning vs. representation learning
- 5. What is representation learning?
- 6. [AAAI2018]Learning Structured Representation Representation for Text Classification via Reinforcemen
- 7. metapath2vec: Scalable Representation Learning for Heterogeneous Networks
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- 10. Truncated Signed Distance Function_Experiments on Voxel Size