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If you find our method/dataset helpful, please consider citing our paper:
@inproceedings{ma2025large,
title={A Large-Scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining},
author={Ma, Qi and Li, Yue and Ren, Bin and Sebe, Nicu and Konukoglu, Ender and Gevers, Theo and Van Gool, Luc and Paudel, Danda Pani},
booktitle={2025 International Conference on 3DV},
pages={145--155},
year={2025},
organization={IEEE}
}
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2D Image/Depth/Normal Rendering of ShapeNet
The image/depth/normal renders are in the ShapeSplat_2d_renders folder, and the camera parameters are saved in per-object transforms.json in the ShapeSplat_render_cams folder. For 2D rendering, we save per-view frame information in the transforms.json for each object, in the format of:
{
"camera_angle_x": 0.6911112070083618,
"frames": [
{
"file_path": "image/000",
"rotation": 0.08726646259971647,
"transform_matrix": [
[
1.0, 0.0, 0.0, 0.0
],
[
0.0, 0.5662031769752502, -0.8242656588554382, -1.0555751323699951
],
[
0.0, 0.8242655992507935, 0.5662031769752502, 0.7250939011573792
],
[
0.0, 0.0, 0.0, 1.0
]
]
},
{ // ... more frames for this object
Camera Intrinsics
The camera intrinsics are calculated from the camera_angle_x field in the transforms JSON file:
def get_intrinsics(camera_angle_x: float, width: int = 400, height: int = 400):
fx = width / (2 * np.tan(camera_angle_x / 2))
fy = fx # Square pixels assumed
cx = width / 2.0 # Principal point at center
cy = height / 2.0
K = [[fx, 0, cx],
[ 0, fy, cy],
[ 0, 0, 1]]
Output:
- Image dimensions: 400×400
- Camera FOV: 39.60°
- Intrinsics matrix:
[[555.56 0 200 ] [ 0 555.56 200 ] [ 0 0 1 ]]
Image Reading
RGB Images:
- Format: PNG files (000.png, 001.png, ...)
- Image in RGBA format (with alpha channel)
- Alpha channel cam be used for background masking
Depth Reading
Depth Maps:
- Format: 4-channel RGBA PNG files from Blender ([frame_id]0001.png, e.g., 0000001.png, 0010001.png, ...)
- Note: Only the first channel (R) contains depth data
- Blender saves depth as inverted values: [0,8] meters → [1,0] normalized
- Script remaps back to linear depth:
depth_linear = depth_min + (1.0 - depth_img) * (depth_max - depth_min) - Background pixels have depth values close to 1.0 (far plane)
Depth Reading:
# Extract first channel from 4-channel depth
depth_img = depth_img_raw[:, :, 0]
# Convert uint8 depth to float normalized to [0, 1]
depth_img = depth_img.astype(np.float32) / 255.0
# Note: Blender remaps [0, 8] to [1, 0]
# Remap depth values from [1, 0] back to [depth_min, depth_max]
depth_min, depth_max = 0, 8
depth_linear = depth_min + (1.0 - depth_img) * (depth_max - depth_min)
valid_mask = (depth_linear > 0.001) & (depth_linear < depth_max - 0.001)
background_mask = depth_img > 0.999
valid_mask = valid_mask & ~background_mask
Coordinate Alignment to 3DGS and OBJ Mesh
Due to coordinate inconsistency at the beginning, the poses of the 2D renderings saved in frame['transform_matrix'] is not aligned to the world coordinate of the 3DGS object and the OBJ mesh.
The following process are needed to convert the 'transform_matrix' key in order to align with the OBJ object mesh.
def convert_cam_coords(transform_matrix):
P = np.array([
[1, 0, 0, 0],
[0, 0, 1, 0],
[0, -1, 0, 0],
[0, 0, 0, 1]
])
C = np.array([
[1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, 1]
])
new_transform_matrix = P @ transform_matrix @ C
return new_transform_matrix
transform_matrix = np.array(frame['transform_matrix'])
transform_matrix = convert_cam_coords(transform_matrix)
After this process, the 2D rendering results will be aligned to the world coordinate as in the original shapenet object, i.e., point_cloud.obj file. For example, the fused depth maps with the processed poses with its point_cloud.obj together:
Additionally, there is misalignment between the released 3DGS object and the corresponding point_cloud.obj file.
To align the 2D rendering results with the released 3DGS, please use the following conversion instead:
# align to the 3dgs object coordinates
def convert_cam_coords(transform_matrix):
C = np.array([
[1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, 1]
])
new_transform_matrix = transform_matrix @ C
return new_transform_matrix
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