VIRDO Dataset (VIRDO Simulated Kitchen Utensil Deformation Dataset)

From https://github.com/MMintLab/VIRDO/blob/master/data/dataset_readme.txt,

1. DESCRIPTION: This dataset is written in 'dtype=torch.float64'. This dataset consists of total 144 deformation scenes from 6 different objects generated through MATLAB. It is divided into 'train' and 'test' dataset, where data['train'][OBJECT IDX = i][DEFORM IDX = j ] and data['test'][OBJECT IDX = i][DEFORM IDX = j ] indicates the same scene, but they are two different subsets of query points.

2. STRUCTURE: The dataset structure is as follows: VIRDO_simul_dataset = { 'train':{ <OBJECT IDX>: { 'nominal': { 'coords': tensor([1, M, 3]), 'normals': tensor([1, M, 3]), 'gt': tensor([1, M, 3]), 'scale': float }, <DEFORM IDX>: { 'coords': tensor([1, M, 3]), 'contact': tensor([1, M_c, 3]), 'normals': tensor([1, M, 3]), 'gt': tensor([1, M, 3]), 'scale': float, 'reaction': tensor([1,3] }, },

   }, 'test':{ <OBJECT IDX>: { 'nominal': { 'coords': tensor([1, M, 3]), 'normals': tensor([1, M, 3]), 'gt': tensor([1, M, 3]), 'scale': float }, <DEFORM IDX>: { 'coords': tensor([1, M, 3]), 'contact': tensor([1, M_c, 3]), 'normals': tensor([1, M, 3]), 'gt': tensor([1, M, 3]), 'scale': float, 'reaction': tensor([1,3] }, },

   }, }

   • <OBJECT IDX> = Interger from 0 ~ 5. Each number indicates different object.
   • <DEFORM IDX> = Unique integer for each deformation.
   • M = total points (on-surface + off-surface)
   • M_c = a subset of on-surface points that are in contact
   • [:,i,:] elements of 'coords', 'normals', and 'gt' refers ith query point of a scene. To get on-surface points of data_def = data['train'][<OBJECT IDX>][<DEFORM IDX>], you should do data_def['coords'][:,torch.where(data_def['gt'] == 0)[1],:].