论文标题
实时，开放式场景理解的功能真实的神经融合
Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding
论文作者
论文摘要
对机器人技术的一般场景理解需要灵活的语义表示，因此可以识别，分割和分组在训练时间可能尚不知道的新颖对象和结构。我们提出了一种算法，该算法将从标准的预训练网络中学习的一般特征融合到实时大满贯期间高效的3D几何神经场表示。融合的3D特征图继承了神经场几何表示的连贯性。这意味着，在运行时相互作用的少量人类标记启用对象甚至对象的一部分可以以开放的方式进行稳健，准确地分割。
General scene understanding for robotics requires flexible semantic representation, so that novel objects and structures which may not have been known at training time can be identified, segmented and grouped. We present an algorithm which fuses general learned features from a standard pre-trained network into a highly efficient 3D geometric neural field representation during real-time SLAM. The fused 3D feature maps inherit the coherence of the neural field's geometry representation. This means that tiny amounts of human labelling interacting at runtime enable objects or even parts of objects to be robustly and accurately segmented in an open set manner.
