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DR-Occluder: Generating Occluders using Differentiable Rendering
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AI
DOI：10.1145/3618346.png)
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摘要
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The target of the occluder is to use very few faces to maintain similar occlusion properties of the original 3D model. In this paper, we present DR-Occluder, a novel coarse-to-fine framework for occluder generation that leverages differentiable rendering to optimize a triangle set to an occluder. Unlike prior work, which has not utilized differentiable rendering for this task, our approach provides the ability to optimize a 3D shape to defined targets. Given a 3D model as input, our method first projects it to silhouette images, which are then processed by a convolution network to output a group of vertex offsets. These offsets are used to transform a group of distributed triangles into a preliminary occluder, which is further optimized by differentiable rendering. Finally, triangles whose area is smaller than a threshold are removed to obtain the final occluder. Our extensive experiments demonstrate that DR-Occluder significantly outperforms state-of-the-art methods in terms of occlusion quality. Furthermore, we compare the performance of our method with other approaches in a commercial engine, providing compelling evidence of its effectiveness.
Keyword:
occluder generation
differentiable rendering
convolution network
geometry simplification
期刊
IF:
9.5
论文数:
4.9K
被引数:
3.6W
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