论文标题
共同监督和自我监督的3D现实世界挑战
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges
论文作者
论文摘要
点云处理和3D形状理解是深度学习技术具有巨大潜力的非常具有挑战性的任务。进一步的进步对于允许人工智能代理人与现实世界互动，在该世界中，带注释的数据的数量可能受到限制，并且整合新知识来源对于支持自主学习至关重要。在这里，我们考虑了涉及合成和现实点云的几种可能场景，由于数据稀缺和较大的域间隙，监督学习失败。我们建议通过通过多任务模型来利用自学模型来丰富标准特征表示，该模型可以在学习形状分类或部分分段的主要任务时解决3D拼图。一项大量研究，调查了几次射击，转移学习和跨域设置，显示了我们的方法的有效性，其最先进的结果对3D形状分类和部分分割。
Point cloud processing and 3D shape understanding are very challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are essential to allow artificial intelligent agents to interact with the real world, where the amount of annotated data may be limited and integrating new sources of knowledge becomes crucial to support autonomous learning. Here we consider several possible scenarios involving synthetic and real-world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation. An extensive analysis investigating few-shot, transfer learning and cross-domain settings shows the effectiveness of our approach with state-of-the-art results for 3D shape classification and part segmentation.
