# Adam SLAM - the last mile of camera calibration with 3DGS

## TL;DR

This paper introduces Adam SLAM, a method that fine-tunes camera calibration using 3DGS and backpropagation to improve novel view synthesis quality, achieving an average 0.4 dB PSNR gain.

## Contribution

It presents a novel calibration refinement technique leveraging 3DGS and backpropagation, enhancing view synthesis accuracy without ground truth.

## Key findings

- Average 0.4 dB PSNR improvement on reference dataset
- Calibration fine-tuning improves novel view synthesis quality
- Method is suitable for reference scene calibration

## Abstract

The quality of the camera calibration is of major importance for evaluating progresses in novel view synthesis, as a 1-pixel error on the calibration has a significant impact on the reconstruction quality. While there is no ground truth for real scenes, the quality of the calibration is assessed by the quality of the novel view synthesis. This paper proposes to use a 3DGS model to fine tune calibration by backpropagation of novel view color loss with respect to the cameras parameters. The new calibration alone brings an average improvement of 0.4 dB PSNR on the dataset used as reference by 3DGS. The fine tuning may be long and its suitability depends on the criticity of training time, but for calibration of reference scenes, such as Mip-NeRF 360, the stake of novel view quality is the most important.