3D geometric information is essential for manipulation tasks, as robots need to perceive the 3D environment, reason about spatial relationships, and interact with intricate spatial configurations. Recent research has increasingly focused on the explicit extraction of 3D features, while still facing challenges such as the lack of large-scale robotic 3D data and the potential loss of spatial geometry. To address these limitations, we propose the Lift3D framework, which progressively enhances 2D foundation models with implicit and explicit 3D robotic representations to construct a robust 3D manipulation policy. Specifically, we first design a task-aware masked autoencoder that masks task-relevant affordance patches and reconstructs depth information, enhancing the 2D foundation model's implicit 3D robotic representation. After self-supervised fine-tuning, we introduce a 2D model-lifting strategy that establishes a positional mapping between the input 3D points and the positional embeddings of the 2D model. Based on the mapping, Lift3D utilizes the 2D foundation model to directly encode point cloud data, leveraging large-scale pretrained knowledge to construct explicit 3D robotic representations while minimizing spatial information loss. In experiments, Lift3D consistently outperforms previous state-of-the-art methods across several simulation benchmarks and real-world scenarios.

本研究解决了机器人在操作任务中对3D几何信息需求的不足，尤其是在缺乏大规模3D数据和空间几何损失的问题上。我们提出的Lift3D框架通过任务感知的掩码自编码器增强2D基础模型的3D隐式表示，并采用2D模型提升策略，利用预训练知识构建显式的3D机器人表示。实验结果显示，Lift3D在多个仿真基准和现实场景中始终优于先前的最先进方法。

Lift3D基础政策：提升2D大规模预训练模型以实现稳健的3D机器人操作