  We propose a new method for object pose estimation without CAD models. The
previous feature-matching-based method OnePose has shown promising results
under a one-shot setting which eliminates the need for CAD models or
object-specific training. However, OnePose relies on detecting repeatable image
keypoints and is thus prone to failure on low-textured objects. We propose a
keypoint-free pose estimation pipeline to remove the need for repeatable
keypoint detection. Built upon the detector-free feature matching method LoFTR,
we devise a new keypoint-free SfM method to reconstruct a semi-dense
point-cloud model for the object. Given a query image for object pose
estimation, a 2D-3D matching network directly establishes 2D-3D correspondences
between the query image and the reconstructed point-cloud model without first
detecting keypoints in the image. Experiments show that the proposed pipeline
outperforms existing one-shot CAD-model-free methods by a large margin and is
comparable to CAD-model-based methods on LINEMOD even for low-textured objects.
We also collect a new dataset composed of 80 sequences of 40 low-textured
objects to facilitate future research on one-shot object pose estimation. The
supplementary material, code and dataset are available on the project page:
https://zju3dv.github.io/onepose_plus_plus/.
