  In recent years, there has been a surge of interest in open-vocabulary 3D
scene reconstruction facilitated by visual language models (VLMs), which
showcase remarkable capabilities in open-set retrieval. However, existing
methods face some limitations: they either focus on learning point-wise
features, resulting in blurry semantic understanding, or solely tackle
object-level reconstruction, thereby overlooking the intricate details of the
object's interior. To address these challenges, we introduce OpenObj, an
innovative approach to build open-vocabulary object-level Neural Radiance
Fields (NeRF) with fine-grained understanding. In essence, OpenObj establishes
a robust framework for efficient and watertight scene modeling and
comprehension at the object-level. Moreover, we incorporate part-level features
into the neural fields, enabling a nuanced representation of object interiors.
This approach captures object-level instances while maintaining a fine-grained
understanding. The results on multiple datasets demonstrate that OpenObj
achieves superior performance in zero-shot semantic segmentation and retrieval
tasks. Additionally, OpenObj supports real-world robotics tasks at multiple
scales, including global movement and local manipulation.
