Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding

Figure 1 . Self-Supervised Feature Visualization using PCA. We reduce the point features obtained with our self-supervised model to three dimensions using PCA and visualize them as colors. Features learned by our model are semantic-aware, which is visible from the color separation: Similar objects result in similar features, such as the sofas in the first figure or the chairs in the last one, while different objects result in different features, such as the counter and the tables in the second image or the crib and the curtains in the third one.