  Neural Radiance Fields (NeRFs) have become a widely-applied scene
representation technique in recent years, showing advantages for robot
navigation and manipulation tasks. To further advance the utility of NeRFs for
robotics, we propose a transformer-based framework, NeRF-Loc, to extract 3D
bounding boxes of objects in NeRF scenes. NeRF-Loc takes a pre-trained NeRF
model and camera view as input and produces labeled, oriented 3D bounding boxes
of objects as output. Using current NeRF training tools, a robot can train a
NeRF environment model in real-time and, using our algorithm, identify 3D
bounding boxes of objects of interest within the NeRF for downstream navigation
or manipulation tasks. Concretely, we design a pair of paralleled transformer
encoder branches, namely the coarse stream and the fine stream, to encode both
the context and details of target objects. The encoded features are then fused
together with attention layers to alleviate ambiguities for accurate object
localization. We have compared our method with conventional RGB(-D) based
methods that take rendered RGB images and depths from NeRFs as inputs. Our
method is better than the baselines.
