neurogs_v2

NeuroGS v2 - 3D Gaussian Splatting for Neuron Reconstruction

This repository contains trained 3D Gaussian Splatting models, checkpoints, and MIP (Maximum Intensity Projection) renderings for neuron reconstruction from microscopy data.

📦 Contents

Checkpoints (103 MB)

• 160+ training checkpoints saved at various iterations (1 to 50,000)
• Final checkpoint: checkpoints/neurogs_codec_ckpt_final.pt (20,450 Gaussians)
• Format: PyTorch .pt files containing Gaussian parameters (positions, scales, rotations, opacities, features)

MIP Projections (5.3 MB)

Ground-truth Maximum Intensity Projections from the original neuron volume:

• 300 views at 256×256 resolution with uniform sphere distribution (Fibonacci lattice)
• Proper aspect ratio preservation with letterboxing
• camera_parameters.json: Full camera metadata (eye position, view matrix, azimuth, elevation, radius)

3DGS Projections - Ray-Based (4.1 MB)

Synthetic MIP projections generated by directly querying the trained 3D Gaussian model:

• 300 views matching Fibonacci sphere camera distribution
• Ray-based rendering: sample 3D points along rays, query Gaussian model, compute max intensity
• camera_parameters.json: Same metadata format

3DGS Projections - Matched Poses (4.2 MB)

Synthetic MIP projections using ground-truth camera poses:

• 300 views with exact camera parameters from mip_projections/camera_parameters.json
• Enables direct comparison between ground-truth volume and 3DGS reconstruction

Volume Data (40 KB)

• 10-2900-control-cell-05_cropped_corrected.swc: Neuron morphology file
• Volume shape: (100, 647, 813) voxels (Z, Y, X)
• Voxel spacing: (1.0, 0.126, 0.126) μm

🔬 Technical Details

3D Gaussian Splatting Model

• 20,450 Gaussians representing the neuron structure
• Trained for 50,000 iterations
• Codec-compressed representation for efficient storage
• Each Gaussian has: 3D position, 3D scale, rotation (quaternion), opacity, appearance features

Camera Configuration

• Uniform sphere sampling: Fibonacci lattice for even distribution
• Camera radius: 1.5 × volume half-diagonal
• Field of view: 50° (default)
• Output resolution: 256×256 pixels (letterboxed to preserve aspect ratio)
• Coordinate system: Camera looks at volume center, up vector aligned with Z-axis

MIP Rendering Methods

1. Ground-truth: Volume rotation + max projection (CUDA-accelerated)
2. Ray-based 3DGS: Direct Gaussian model querying along rays
3. MIP Splatting: 2D Gaussian projection (future implementation)

📊 Dataset Statistics

🚀 Usage

Load Checkpoint

import torch
from model import GaussianMixtureVolume

checkpoint = torch.load('checkpoints/neurogs_codec_ckpt_final.pt')
model = GaussianMixtureVolume(...)
model.load_state_dict(checkpoint, strict=False)

Load Camera Parameters

import json

with open('mip_projections/camera_parameters.json', 'r') as f:
    cameras = json.load(f)

# Access camera for view 0
cam = cameras[0]
eye_pos = cam['eye']               # [x, y, z]
view_matrix = cam['view_matrix_4x4']  # 4×4 matrix
azimuth = cam['azimuth_deg']       # degrees
elevation = cam['elevation_deg']   # degrees

Render MIP from 3DGS Model

See notebook/render_3dgs_mip.ipynb for complete examples:

• Ray-based rendering
• MIP splatting (2D projection)
• Camera pose matching
• Batch processing

📝 Camera Parameters Schema

Each JSON entry contains:

{
  "eye": [x, y, z],                    // Camera position in world space
  "target": [x, y, z],                  // Look-at point (volume center)
  "view_matrix_4x4": [[...], ...],      // 4×4 view transformation matrix
  "azimuth_deg": 0.0,                   // Azimuth angle (0-360°)
  "elevation_deg": 26.57,               // Elevation angle (-90 to 90°)
  "sphere_point": [x, y, z],            // Normalized direction on unit sphere
  "camera_radius": 1234.56,             // Distance from volume center
  "volume_shape_ZYX": [100, 647, 813],  // Original volume dimensions
  "mip_shape_HW": [256, 256],           // Output image size
  "projection_type": "MIP",             // Rendering method
  "rendering_method": "..."             // Specific algorithm used
}

🔗 Related Files

For the complete training pipeline and rendering scripts, see the main repository:

• render_mip_projections.py: Generate ground-truth MIPs
• notebook/render_3dgs_mip.ipynb: 3DGS synthesis notebook
• model.py: GaussianMixtureVolume implementation
• train.py: Training loop

📄 License

This dataset is released for research purposes. Please cite the NeuroGS paper if you use this data in your research.

🙏 Acknowledgments

• Built on 3D Gaussian Splatting
• Neuron data from microscopy imaging
• Fibonacci sphere sampling for uniform camera distribution

📧 Contact

For questions or issues, please open an issue in the repository.

---

Total Dataset Size: ~117 MB
Upload Date: February 13, 2026
Model Version: v2
Training Iterations: 50,000