POMA-3D: The Point Map Way to 3D Scene Understanding

POMA-3D has been introduced as the first self-supervised 3D representation model that learns from point maps, which encode explicit 3D coordinates on a structured 2D grid. This model preserves global 3D geometry and is compatible with 2D foundation models, utilizing a view-to-scene alignment strategy to enhance its capabilities.
The development of POMA-3D is significant as it serves as a robust backbone for various 3D understanding tasks, including 3D question answering and embodiment, thereby advancing the field of artificial intelligence in scene understanding.
This innovation aligns with ongoing efforts in the AI community to improve 3D tracking and representation, as seen in methods like TAPIP3D, which focuses on long-term tracking in RGB and RGB-D videos. The integration of 2D and 3D data is becoming increasingly important in enhancing the accuracy and applicability of AI models in real-world scenarios.

POMA-3D: The Point Map Way to 3D Scene Understanding