  Large visual-language models (VLMs), like CLIP, enable open-set image
segmentation to segment arbitrary concepts from an image in a zero-shot manner.
This goes beyond the traditional closed-set assumption, i.e., where models can
only segment classes from a pre-defined training set. More recently, first
works on open-set segmentation in 3D scenes have appeared in the literature.
These methods are heavily influenced by closed-set 3D convolutional approaches
that process point clouds or polygon meshes. However, these 3D scene
representations do not align well with the image-based nature of the
visual-language models. Indeed, point cloud and 3D meshes typically have a
lower resolution than images and the reconstructed 3D scene geometry might not
project well to the underlying 2D image sequences used to compute pixel-aligned
CLIP features. To address these challenges, we propose OpenNeRF which naturally
operates on posed images and directly encodes the VLM features within the NeRF.
This is similar in spirit to LERF, however our work shows that using pixel-wise
VLM features (instead of global CLIP features) results in an overall less
complex architecture without the need for additional DINO regularization. Our
OpenNeRF further leverages NeRF's ability to render novel views and extract
open-set VLM features from areas that are not well observed in the initial
posed images. For 3D point cloud segmentation on the Replica dataset, OpenNeRF
outperforms recent open-vocabulary methods such as LERF and OpenScene by at
least +4.9 mIoU.
