| ModelNet_Splats release (Fall 2024) | |
| Each zip file contains ply files where each Gaussian is encoded as a vertex with custom vertex attributes. This ply format is commonly used for Gaussian splats and can be viewed using [online viewer](https://playcanvas.com/supersplat/editor/). | |
| To open the .ply files, you can use the following python code: | |
| ```python | |
| from plyfile import PlyData | |
| import numpy as np | |
| gs_vertex = PlyData.read('ply_path')['vertex'] | |
| ### load centroids[x,y,z] - Gaussian centroid | |
| x = gs_vertex['x'].astype(np.float32) | |
| y = gs_vertex['y'].astype(np.float32) | |
| z = gs_vertex['z'].astype(np.float32) | |
| centroids = np.stack((x, y, z), axis=-1) # [n, 3] | |
| ### load o - opacity | |
| opacity = gs_vertex['opacity'].astype(np.float32).reshape(-1, 1) | |
| ### load scales[sx, sy, sz] - Scale | |
| scale_names = [ | |
| p.name | |
| for p in gs_vertex.properties | |
| if p.name.startswith("scale_") | |
| ] | |
| scale_names = sorted(scale_names, key=lambda x: int(x.split("_")[-1])) | |
| scales = np.zeros((centroids.shape[0], len(scale_names))) | |
| for idx, attr_name in enumerate(scale_names): | |
| scales[:, idx] = gs_vertex[attr_name].astype(np.float32) | |
| ### load rotation rots[q_0, q_1, q_2, q_3] - Rotation | |
| rot_names = [ | |
| p.name for p in gs_vertex.properties if p.name.startswith("rot") | |
| ] | |
| rot_names = sorted(rot_names, key=lambda x: int(x.split("_")[-1])) | |
| rots = np.zeros((centroids.shape[0], len(rot_names))) | |
| for idx, attr_name in enumerate(rot_names): | |
| rots[:, idx] = gs_vertex[attr_name].astype(np.float32) | |
| rots = rots / (np.linalg.norm(rots, axis=1, keepdims=True) + 1e-9) | |
| ### load base sh_base[dc_0, dc_1, dc_2] - Spherical harmonic | |
| sh_base = np.zeros((centroids.shape[0], 3, 1)) | |
| sh_base[:, 0, 0] = gs_vertex['f_dc_0'].astype(np.float32) | |
| sh_base[:, 1, 0] = gs_vertex['f_dc_1'].astype(np.float32) | |
| sh_base[:, 2, 0] = gs_vertex['f_dc_2'].astype(np.float32) | |
| sh_base = sh_base.reshape(-1, 3) | |
| ``` | |
| Note that more details regarding <u>PSNR</u>, <u>SSIM</u>, <u>LPIPS</u>, <u>FILE_SIZE</u> and <u>Number of Gaussians</u> can be find in [summary](ShapeSplatsV1_Qualitative_Results.json). To showcase the difference of point cloud in spatial distribution, we report Jensen–Shannon Divergence (JSD) and Maximum Mean Discrepancy (MMD) between gaussian splats and ShapeNetCoreV1 point clouds. | |
| More details for the pretraining method of ShapeSplats can be find in [Gaussian-MAE](https://unique1i.github.io/ShapeSplat/) | |
| Last updated: 2024-09-05 | |