auniquesun/Point-PRC

Datasets

1. We conduct experiments on three new 3D domain generalization (3DDG) benchmarks proposed by us, as introduced in the next section.

   • base-to-new class generalization (base2new)
   • cross-dataset generalization (xset)
   • few-shot generalization (fewshot)

2. The structure of these benchmarks should be organized as follows.

    /path/to/Point-PRC
    |----data # placed in the same level of `trainers`, `weights`, etc. 
        |----base2new
            |----modelnet40
            |----scanobjectnn
            |----shapenetcorev2
        |----xset
            |----corruption
            |----dg
            |----sim2real
            |----pointda
        |----fewshot
            |----modelnet40
            |----scanobjectnn
            |----shapenetcorev2

3. You can find the usage instructions and download links of these new 3DDG benchmarks in the following section.

New 3DDG Benchmarks

Base-to-new Class Generalization

1. The datasets used in this benchmark can be downloaded according to the following links.

   • ModelNet40
   • S-OBJ_ONLY
   • S-OBJ_BG
   • S-PB_T50_RS
   • ShapeNetCoreV2

2. The following table shows the statistics of this benchmark.

   

Cross-dataset Generalization

1. The datasets used in this benchmark can be downloaded according to the following links.

   • OOD Generalization
     • OmniObject3d (Omin3D)
   • Data Corruption
     • ModelNet-C (7 types of corruptions)
       • add global outliers, add local outliers, dropout global structure, dropout local region, rotation, scaling, jittering
   • Sim-to-Real
   • PointDA

2. The following table shows the statistics of this benchmark.

   

Few-shot Generalization

1. Although this benchmark contains same datasets as the Base-to-new Class, it investigates the model generalization under extremely low-data regime (1, 2, 4, 8, and 16 shots), which is quite different from the evaluation setting in Base-to-new Class Generalization.

2. The following table shows the statistics of this benchmark.