swami1995 / V2V

Code for "From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers"

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Overview

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Installation and Evaluation

For installation and evaluation instructions please check README_DPVO.md, as the instructions are the same for both setups.

Training

To train vanilla DPVO run the following command (log files will be written to runs/<your name>).

python train.py --steps=240000 --lr=0.00008 --name=<your name> --so_flag

Model will be run on the validation split every 10k iterations.

To train the weighted version, run the following command

python train.py --steps=240000 --lr=0.0004 --name=<your name> --wtd_obj

To train the non-streaming version with 8 inner loop steps, run the following

python train.py --steps=240000 --lr=0.00008 --name=<your name> --so_flag --iters 8 --n_frames 8

To train the corresponding weighted version, run the following,

python train.py --steps=240000 --lr=0.00008 --name=<your name> --wtd_obj --iters 8 --n_frames 8

About

Code for "From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers"


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