GANmouflage：基于纹理场的3D物体不可检测性
GANmouflage: 3D Object Nondetection with Texture Fields
AI总结：
本文提出GANmouflage，结合纹理场与对抗学习，为不同3D形状生成可在多视点场景中规避检测的伪装纹理，并经人类视觉搜索实验验证其显著优于既有方法。
摘要：
我们提出了一种学习在场景中伪装3D物体的方法。给定一个物体的形状以及观察该物体的视点分布，我们估计一种纹理，使其难以被检测到。成功解决这一任务需要一个能够准确再现场景纹理的模型，同时还要处理每个视点所施加的高度冲突的约束。我们通过一个基于纹理场和对抗学习的模型来应对这些挑战。我们的模型学习从输入场景内随机采样的位置和视点伪装各种物体形状，并且是首个解决隐藏复杂物体形状问题的方法。通过一项人类视觉搜索研究，我们发现，与以往方法相比，我们估计的纹理能显著更好地隐藏物体。项目网站：https://rrrrrguo.github.io/ganmouflage/
英文摘要：
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. Project site: https://rrrrrguo.github.io/ganmouflage/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
