Although Neural Radiance Fields (NeRF) is popular in the computer vision
community recently, registering multiple NeRFs has yet to gain much attention.
Unlike the existing work, NeRF2NeRF, which is based on traditional optimization
methods and needs human annotated keypoints, we propose DReg-NeRF to solve the
NeRF registration problem on object-centric scenes without human intervention.
After training NeRF models, our DReg-NeRF first extracts features from the
occupancy grid in NeRF. Subsequently, our DReg-NeRF utilizes a transformer
architecture with self-attention and cross-attention layers to learn the
relations between pairwise NeRF blocks. In contrast to state-of-the-art (SOTA)
point cloud registration methods, the decoupled correspondences are supervised
by surface fields without any ground truth overlapping labels. We construct a
novel view synthesis dataset with 1,700+ 3D objects obtained from Objaverse to
train our network. When evaluated on the test set, our proposed method beats
the SOTA point cloud registration methods by a large margin, with a mean
$\text{RPE}=9.67^{\circ}$ and a mean $\text{RTE}=0.038$.
Our code is available at this https URL

通过使用 DReg-NeRF 方法，我们能够在不需要人为干预的情况下解决对象中心场景的 NeRF 注册问题，并通过构建一个新颖的视图综合数据集进行训练，从而在测试集上显著超过目前最先进的点云注册方法。