Lifting Unlabeled Internet-level Data for 3D Scene Understanding

Abstract

Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled videos are readily available on the internet. In this paper, we demonstrate that carefully designed data engines can leverage web-curated, unlabeled videos to automatically generate training data, to facilitate end-to-end models in 3D scene understanding alongside human-annotated datasets. We identify and analyze bottlenecks in automated data generation, revealing critical factors that determine the efficiency and effectiveness of learning from unlabeled data. To validate our approach across different perception granularities, we evaluate on three tasks spanning low-level perception, i.e., 3D object detection and instance segmentation, to high-evel reasoning, i.e., 3D spatial Visual Question Answering (VQA) and Vision-Lanugage Navigation (VLN). Models trained on our generated data demonstrate strong zero-shot performance and show further improvement after finetuning. This demonstrates the viability of leveraging readily available web data as a path toward more capable scene understanding systems.

Figures (12)

• Figure 1: Overview of SceneVerse++. From unlabeled internet videos, we build automated data engines to create training data for comprehensive 3D scene understanding, realizing strong zero-shot performance on existing benchmarks, with further improvement after finetuning. This pinpoints future direction towards 3D spatial intelligence through improved automation on unlabeled, web-scale data.
• Figure 2: Statistics comparison. SceneVerse++ encompasses more scenes, larger areas, and greater object diversity compared with existing real-world datasets.
• Figure 3: Overview of data generation. The pipeline leverages a modular design for automatic 3D reconstruction and segmentation.
• Figure 4: Reconstruction and segmentation comparison, where SceneVerse++ features a balance in quality and efficiency.
• Figure 5: Training dynamics.