Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast

Understand

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.