GANmouflage: 3D Object Nondetection With Texture Fields

Rui Guo, Jasmine Collins, Oscar de Lima, Andrew Owens; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 4702-4712

Abstract


We propose a method that learns to camouflage 3D objects within scenes. Given an object's shape and a distribution of viewpoints from which it will be seen, we estimate a texture that will make it difficult to detect. Successfully solving this task requires a model that can accurately reproduce textures from the scene, while simultaneously dealing with the highly conflicting constraints imposed by each viewpoint. We address these challenges with a model based on texture fields and adversarial learning. Our model learns to camouflage a variety of object shapes from randomly sampled locations and viewpoints within the input scene, and is the first to address the problem of hiding complex object shapes. Using a human visual search study, we find that our estimated textures conceal objects significantly better than previous methods.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Guo_2023_CVPR, author = {Guo, Rui and Collins, Jasmine and de Lima, Oscar and Owens, Andrew}, title = {GANmouflage: 3D Object Nondetection With Texture Fields}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {4702-4712} }