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On one hand, I agree with your point on further improving the performance using better representations, better base models and geometry-aware losses. And I believe there will always some new techniques come out for improving the base model performance.
On the other hand, although they are important, they are not the priority of Real3D. In this project, we want to deliver our philosophy of scaling up models/training data by self-training on in-the-wild data, which is orthogonal to developing a better base model. We hope this philosophy can help any future models.
很棒的工作,有点像depth-anything的思路,先在高质量synthetic data上训练, 再迁移到real image上。 不过出来的模型感觉纹理和shape都比较粗糙,感觉是不是可以把triplane换掉,比如(UDF, occupancy field?),triplane的分辨率会成为模型的bottleneck。
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