3 accepted papers
Most industrial parts are designed from parametric shapes with the properties of diversity and uncertainty. We propose a 6DoF pose estimation network, ParametricNet++, based on pointwise regression and sparse keypoint recovery, which is extended from ParametricNet to include optimizations of keypoin…
Training with simulated data is a common approach in pose estimation research. However, a sim-to-real gap between clean simulated data and noisy real data will seriously weaken the generalization ability of the algorithm, especially for point clouds. To address this problem, this paper proposes a do…
Most industrial parts are parametric and their special properties are not fully explored yet. This paper proposes a new 6DoF pose estimation network for parametric shapes in stacked scenarios (ParametricNet). It treats a parametric shape, instead of a part object, as a category. The keypoints of ind…