ba-cuda 0.1.0

GPU-accelerated Bundle Adjustment using PyTorch with Analytical Jacobians

ba-cuda

GPU-accelerated Bundle Adjustment using PyTorch with Analytical Jacobians.

Features

• SE3 Lie Group: Right perturbation convention for camera pose representation
• Analytical Jacobians: Fast and accurate gradient computation
• Batched Operations: Efficient GPU parallelization
• Levenberg-Marquardt Optimizer: Robust non-linear optimization
• Huber Loss: Robust to outliers

Installation

1. Install PyTorch (Required - Manual Installation)

⚠️ Important: PyTorch must be installed manually according to your CUDA version.

1. Check your CUDA version:

   nvcc --version
   

2. Install PyTorch from the official website:

   For example, with CUDA 12.1:

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
   

   For CUDA 11.8:

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   

2. Install ba-cuda

pip install ba-cuda

Or from source:

git clone https://github.com/hunminkim98/BA_on_CUDA_130_Win.git
cd BA_on_CUDA_130_Win
pip install -e .

Quick Start

import torch
from ba_cuda import LevenbergMarquardtOptimizer

# Prepare your data
observations = torch.tensor(...)      # (N_obs, 2) - 2D observations
points_3d = torch.tensor(...)         # (N_obs, 3) - 3D points
camera_indices = torch.tensor(...)    # (N_obs,) - camera index per observation
intrinsics = torch.tensor(...)        # (N_cams, 3, 3)
distortions = torch.tensor(...)       # (N_cams, 5)
initial_poses = torch.tensor(...)     # (N_cams, 3, 4) - [R|t]

# Run optimization
optimizer = LevenbergMarquardtOptimizer()
optimized_poses, rmse = optimizer.optimize(
    observations=observations,
    points_3d=points_3d,
    camera_indices=camera_indices,
    intrinsics=intrinsics,
    distortions=distortions,
    initial_poses=initial_poses,
    ref_cam_idx=0
)

print(f"Final RMSE: {rmse:.4f}")

API Reference

LevenbergMarquardtOptimizer

optimizer = LevenbergMarquardtOptimizer(
    max_iterations=50,      # Maximum LM iterations
    abs_tolerance=1e-6,     # Absolute convergence tolerance
    rel_tolerance=1e-6,     # Relative convergence tolerance
    initial_damping=1e-3,   # Initial LM damping factor
    huber_delta=1.0,        # Huber loss delta
    device=None,            # torch.device (auto-detects CUDA if available)
    dtype=torch.float64     # Precision
)

Input Tensor Formats

Tensor	Shape	Description
observations	(N_obs, 2)	2D keypoint observations (u, v)
points_3d	(N_obs, 3)	3D points in reference camera frame
camera_indices	(N_obs,)	Camera index for each observation
intrinsics	(N_cams, 3, 3)	Camera intrinsic matrices K
distortions	(N_cams, 5)	Distortion coefficients [k1, k2, p1, p2, k3]
initial_poses	(N_cams, 3, 4)	Initial camera poses [R

Requirements

• Python >= 3.10
• PyTorch >= 2.0.0 (with CUDA support recommended)
• NumPy >= 1.20.0

License

MIT License