论文标题
场景的预测合理性
Prediction of Scene Plausibility
论文作者
论文摘要
从2D图像中了解3D世界不仅涉及场景中对象的检测和分割。它还包括对场景元素的结构和布置的解释。这种理解通常植根于认识物理世界及其局限性，以及关于如何布置类似场景的先验知识。在这项研究中，我们对理解算法的神经网络（或其他场景）提出了一个新的挑战 - 它们可以区分合理的场景和令人难以置信的场景吗？可以根据物理特性以及功能和典型布置来定义合理性。因此，我们将合理性定义为在真实的物理世界中遇到给定场景的可能性。我们构建了一个包含合理和难以置信的场景的合成图像的数据集，并在识别和理解合理性的任务中测试了各种视觉模型的成功。
Understanding the 3D world from 2D images involves more than detection and segmentation of the objects within the scene. It also includes the interpretation of the structure and arrangement of the scene elements. Such understanding is often rooted in recognizing the physical world and its limitations, and in prior knowledge as to how similar typical scenes are arranged. In this research we pose a new challenge for neural network (or other) scene understanding algorithms - can they distinguish between plausible and implausible scenes? Plausibility can be defined both in terms of physical properties and in terms of functional and typical arrangements. Hence, we define plausibility as the probability of encountering a given scene in the real physical world. We build a dataset of synthetic images containing both plausible and implausible scenes, and test the success of various vision models in the task of recognizing and understanding plausibility.
