Contrastive learning has recently demonstrated great potential for unsupervised pre-training in 3D scene understanding tasks. However, most existing work randomly selects point features as anchors while building contrast, leading to a clear bias toward background points that often dominate in 3D scenes. Also, object awareness and foreground-to-background discrimination are neglected, making contrastive learning less effective. To tackle these issues, we propose a general foreground-aware feature contrast FAC++ framework to learn more effective point cloud representations in pre-training. FAC++ consists of two novel contrast designs to construct more effective and informative contrast pairs. The first is building positive pairs within the same foreground segment where points tend to have the same semantics. The second is that we prevent over-discrimination between 3D segments/objects and encourage grouped foreground-to-background distinctions at the segment level with adaptive feature learning in a Siamese correspondence network, which adaptively learns feature correlations within and across point cloud views effectively. Moreover, we have designed the foreground-prompted regional sampling to enhance more balanced foreground-aware learning, which is termed FAC++. Visualization with point activation maps shows that our contrast pairs capture clear correspondences among foreground regions during pre-training. Quantitative experiments also show that FAC++ achieves superior knowledge transfer and data efficiency in various downstream 3D semantic segmentation, instance segmentation as well as object detection tasks. All codes, data, and models are available at: https://github.com/KangchengLiu/FAC_Foreground_Aware_Contrast

翻译：对比学习近期在三维场景理解任务的无监督预训练中展现出巨大潜力。然而，现有工作大多随机选取点特征作为对比锚点，导致明显偏向于在三维场景中占据主导的背景点。同时，目标感知和前景-背景区分被忽视，使得对比学习效果受限。为解决这些问题，我们提出通用前景感知特征对比FAC++框架，用于在预训练中学习更有效的点云表征。FAC++包含两种新颖的对比设计，以构建更有效且信息丰富的对比对：第一，在同一前景片段内构建正样本对——该区域内的点倾向于具有相同语义；第二，通过孪生对应网络中的自适应特征学习，防止三维片段/目标间的过度区分，并在片段层级上促进分组的前景-背景辨别，从而有效学习点云视角内外部的特征相关性。此外，我们设计了前景引导的局部区域采样以增强更均衡的前景感知学习，该方法命名为FAC++。基于点激活映射的可视化表明，我们的对比对能在预训练中捕获前景区域间的清晰对应关系。定量实验也显示，FAC++在多种下游三维语义分割、实例分割及目标检测任务中实现了卓越的知识迁移与数据效率。所有代码、数据和模型均发布于：https://github.com/KangchengLiu/FAC_Foreground_Aware_Contrast