DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing
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Date
2020Type
- Conference Paper
ETH Bibliography
yes
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Abstract
We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function. Due to the nature of the implicit function, the rendering process requires tremendous function queries, which is particularly problematic when the function is represented as a neural network. We optimize both the forward and backward pass of our rendering layer to make it run efficiently with affordable memory consumption on a commodity graphics card. Our rendering method is fully differentiable such that losses can be directly computed on the rendered 2D observations, and the gradients can be propagated backward to optimize the 3D geometry. We show that our rendering method can effectively reconstruct accurate 3D shapes from various inputs, such as sparse depth and multi-view images, through inverse optimization. With the geometry based reasoning, our 3D shape prediction methods show excellent generalization capability and robustness against various noises. Show more
Publication status
publishedExternal links
Book title
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.More
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yes
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