论文标题
ganmouflage：3D对象无纹理字段
GANmouflage: 3D Object Nondetection with Texture Fields
论文作者
论文摘要
我们提出了一种学会在场景中伪装3D对象的方法。鉴于对象的形状和观点的分布，我们可以估计一种质地，该纹理将难以检测。成功解决此任务需要一个模型，该模型可以准确地从场景中复制纹理，同时处理每个观点所施加的高度冲突的约束。我们通过基于纹理领域和对抗性学习的模型来应对这些挑战。我们的模型学会了从输入场景中的随机采样位置和观点伪装各种对象形状，并且是第一个解决隐藏复杂对象形状的问题的问题。使用人类的视觉搜索研究，我们发现我们的估计纹理掩盖对象的掩盖比以前的方法要好得多。项目网站：https：//rrrrrrguo.github.io/ganmouflage/
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/
