python==3.10
numpy
torch
scipy
cv2

The above is the data folder setup for the experiments. Do follow the above structure so that the source code runs seamlessly

1. mkdir /path/to/lidar-camera-alignment/data/batch_test
2. This will be the path where the experiment results will be saved (visualization and logging mostly)

1. Download the zip files from https://www.cvlibs.net/datasets/kitti/eval_odometry.php
2. We are only working with sequence 00 of the KITTI dataset.
3. Unzip it to path/to/lidar-camera-alignment/data/labels

1. Get it from https://drive.google.com/file/d/1DLJtCda9fjfWkGyhORbWo0uCAWQcfwgV/view?usp=sharing
2. Unzip it to path/to/lidar-camera-alignment/data/labels

main.py

    # Parse hyperparameters.
    rot_param_type = 'euler'  # 'axis_angle', 'euler', 'quaternion'
    lr = 1e-6  # learning rate
    rotation_degrees = dict(z=10, y=10, x=10)  # rotation to be introduced
    num_iter = 1000  # How many iterations to run the optimization.
    image_labels_subsampling_factor = 5.  # How sparse the image label will be.
    depth_scaling_factor = 1.  # How much weight to be given to the depth value as compared to the xy positions
    num_samples = 10  # How many samples to run a single batch optimization
    data_dump_dir = "../data/batch_test/"  # Where to dump your experiment results

cd /path/to/lidar-camera-alignment/src
python main.py

• Loss logging. @Sergi
• Convergence criteria @Sergi
• Add and test translation parameterization @Sergi
• Bag loss to a file
• Experiment with different transformations (4x4) @Dhanesh
• Addition of point level lidar labels. @Hari
• Adding more geometric priors (unprojection) @Dhanesh @Hari
• Explore more robust label processing more (use confidence scores or) @Shreyas @Hari

Stretch goals

• Blooming of image seglabels. @Sergi

1. The effect of the number of batches for the optimization profile
2. The effect of the diversity of the batch for a single optimization update
3. The effect of rotation parameterization in the optimization profile
4. The effect of learning rate in the optimization profile
5. How sensitive is the model towards bad initial guesses
6. The effect of using depth if any.

1. Addition of depth
2. Addition of batch optimization
3. Addition of efficient image label processing.