用3DGS反向优化相机参数，提升图像重建质量。
Adam SLAM - the last mile of camera calibration with 3DGS
- 用3DGS的视角损失反向调整相机参数，实现端到端校准。
- 在3DGS基准数据集上平均提升0.4 dB PSNR，显著改善重建质量。
- 适合对视图合成精度要求极高的场景校准，如Mip-NeRF 360。
相机校准质量对新视角合成性能评估至关重要，因为1像素的校准误差会对重建质量产生显著影响。由于真实场景无真值，校准质量通常通过新视角合成效果来评估。本文提出利用3DGS模型，通过新视角颜色损失对相机参数进行反向传播微调。该方法单独使用即可在3DGS参考数据集上带来平均0.4 dB PSNR提升。尽管微调过程可能耗时，但针对如Mip-NeRF 360等参考场景的校准，新视角质量至关重要，因此具有重要应用价值。
原文摘要 · Abstract (English)
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.
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