There exist a handful of techniques to tackle geometric transformations of 3D point cloud data in the context of 3D object recognition.

Curved objects pose a fundamental challenge for skill transfer in robotics: unlike planar surfaces, they do not admit a global reference frame. As a result, task-relevant directions such as "toward" or "along" the surface vary with position and geometry, making object-centric tasks difficult to transfer across shapes. To address this, we introduce an approach using Diffused Orientation Fields (DOF), a smooth representation of local reference frames, for transfer learning of tasks across curved objects. By expressing manipulation tasks in these smoothly varying local frames, we reduce the problem of transferring tasks across curved objects to establishing sparse keypoint correspondences. DOF is computed online from raw point cloud data using diffusion processes governed by partial differential equations, conditioned on keypoints. We evaluate DOF under geometric, topological, and localization perturbations, and demonstrate successful transfer of tasks requiring continuous physical interaction such as inspection, slicing, and peeling across varied objects. We provide our open-source codes at our website https://github.com/idiap/diffused_fields_robotics

Lorentz-equivariant neural networks are becoming the leading architectures for high-energy physics. Current implementations rely on specialized layers, limiting architectural choices. We introduce Lorentz Local Canonicalization (LLoCa), a general framework that renders any backbone network exactly Lorentz-equivariant. Using equivariantly predicted local reference frames, we construct LLoCa-transformers and graph networks. We adapt a recent approach to geometric message passing to the non-compact Lorentz group, allowing propagation of space-time tensorial features. Data augmentation emerges from LLoCa as a special choice of reference frame. Our models surpass state-of-the-art accuracy on relevant particle physics tasks, while being $4\times$ faster and using $5$-$100\times$ fewer FLOPs.

Weaknesses: The idea of estimating and relying on local reference frame to achieve rotation invariance has been explored before in similar context, thus might downgrade the novelty of this paper. For example, "A-CNN: Annularly Convolutional Neural Networks on Point Clouds, CVPR'19" uses the local point set to estimate the normal as this paper does, the difference is that A-CNN uses this normal to project 3d points into 2d plane, however, the basic idea of them is both to achieve locally rotation invariance. "Relation-Shape Convolutional Neural Network for Point Cloud Analysis, CVPR'19" mentioned in their experiments about rotation invariance that they construct a local reference frame to achieve rotation invariant representation of local point set which is the same as this paper. The randomized technique is also a common technique in training deep networks for exploring a larger data space or parameter space. The whole hierarchy is identical to PointNet .