Abstract
With the rapid advancement of technology, 3D data acquisition and utilization have become increasingly prevalent across various fields, including computer vision, robotics, and geospatial analysis. 3D data, captured through methods such as 3D scanners, LiDARs, and RGB-D cameras, provides rich geometric, shape, and scale information. When combined with 2D images, 3D data offers machines a comprehensive understanding of their environment, benefiting applications like autonomous driving, robotics, remote sensing, and medical treatment. This dissertation focuses on three main areas: supervised representation learning for point cloud primitive segmentation, self-supervised learning methods, and transfer learning from 2D to 3D. Our approach, which integrates pre-trained 2D models to support 3D network training, significantly improves 3D understanding without merely transforming 2D data. Extensive experiments validate the effectiveness of our methods, showcasing their potential to advance point cloud representation learning by effectively integrating 2D knowledge.
Abstract (translated)
随着技术的迅速发展，三维数据采集和应用在计算机视觉、机器人技术和地理空间分析等各个领域变得越来越普遍。通过3D扫描仪、LiDAR（激光雷达）以及RGB-D相机捕捉到的三维数据提供了丰富的几何形状、形态及尺度信息。当与二维图像结合时，三维数据能够为机器提供对其环境的整体理解，从而在自动驾驶、机器人技术、遥感和医疗治疗等领域发挥重要作用。 本论文主要关注三个领域：点云基础分割中的监督表示学习、自我监督学习方法以及从2D到3D的迁移学习。我们的研究方法整合了预训练的二维模型来支持三维网络的训练，在不单纯转换二维数据的情况下，显著提升了对三维环境的理解能力。广泛的实验验证了我们所提出的方法的有效性，并展示了其在有效融合二维知识以推进点云表示学习方面的巨大潜力。
URL
https://arxiv.org/abs/2512.06058
