ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their
  Self-Supervised Pretraining
Authors: name: Qi Ma, name: Yue Li, name: Bin Ren, name: Nicu Sebe, name: Ender Konukoglu, name: Theo Gevers, name: Luc Van Gool, name: Danda Pani Paudel

3D Gaussian Splatting (3DGS) has become the de facto method of 3D
representation in many vision tasks. This calls for the 3D understanding
directly in this representation space. To facilitate the research in this
direction, we first build a large-scale dataset of 3DGS using the commonly used
ShapeNet and ModelNet datasets. Our dataset ShapeSplat consists of 65K objects
from 87 unique categories, whose labels are in accordance with the respective
datasets. The creation of this dataset utilized the compute equivalent of 2 GPU
years on a TITAN XP GPU.
  We utilize our dataset for unsupervised pretraining and supervised finetuning
for classification and segmentation tasks. To this end, we introduce
\textit{Gaussian-MAE}, which highlights the unique benefits of
representation learning from Gaussian parameters. Through exhaustive
experiments, we provide several valuable insights. In particular, we show that
(1) the distribution of the optimized GS centroids significantly differs from
the uniformly sampled point cloud (used for initialization) counterpart; (2)
this change in distribution results in degradation in classification but
improvement in segmentation tasks when using only the centroids; (3) to
leverage additional Gaussian parameters, we propose Gaussian feature grouping
in a normalized feature space, along with splats pooling layer, offering a
tailored solution to effectively group and embed similar Gaussians, which leads
to notable improvement in finetuning tasks.