Recent research has shown that generative models with highly disentangled representations fail to generalise to unseen combination of generative factor values.

Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy that best fits observed sequences of states and actions implemented byanexpert.

In this paper, we theoretically and empirically characterize obstructions to training encoders with geometric latent spaces. We show that local optima can arise due to singularities (e.g.

In each hierarchy, the number of neighbors for the stochastic dilatedk-NNs, denoted byk, is sampled from the following three different uniform distributions:U(32,96),U(16,48),andU(8,24). Meanwhile,thenumberofedgesinthe graph, ˆk, is given by sampling fromU(8,24),U(4,12), andU(2,8). Thedilationrateemployed in the first descriptor extraction stage is sampled fromU(2,4). Figure 1 visualizes the local axes constructed in the descriptor extraction step. The figures on the left present the original point clouds and the right ones illustrate the local reference frames for the downsampled points.

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

Additionally, for FVDcalculation, we generate 256 videosforevaluation.