Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An Analytical Solution to Gauss-Newton Loss for Direct Image Alignment
Authors: Sergei Solonets, Daniil Sinitsyn, Lukas Von Stumberg, Nikita Araslanov, Daniel Cremers
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on two most popular datasets for large-scale image localization, namely the Aachen Day-Night dataset (Sattler et al., 2018), extended CMU seasons (Toft et al., 2022) and 7Scenes dataset (Shotton et al., 2013). Table 1 presents the results for camera localization on Aachen Day-Night (Sattler et al., 2018) and the extended CMU Seasons (Toft et al., 2022). |
| Researcher Affiliation | Collaboration | Sergei Solonets1,2, Daniil Sinitsyn1,2, Lukas von Stumberg3, Nikita Araslanov1,2 Daniel Cremers1,2 1 Technical University of Munich 2 Munich Center for Machine Learning 3 Valve Software |
| Pseudocode | Yes | Algorithm 1: Image alignment. |
| Open Source Code | Yes | Project code: https://github.com/tum-vision/gn_loss_analytical. To facilitate reproducibility in future research, we also publicly release our code. |
| Open Datasets | Yes | We evaluate our approach on two most popular datasets for large-scale image localization, namely the Aachen Day-Night dataset (Sattler et al., 2018), extended CMU seasons (Toft et al., 2022) and 7Scenes dataset (Shotton et al., 2013). |
| Dataset Splits | No | The paper evaluates its approach on established datasets and uses self-supervised feature descriptors like Super Point. However, it does not explicitly provide training/validation/test dataset splits for its own method or the pre-trained feature extractors, which are used as components. |
| Hardware Specification | Yes | On average, the code takes approximately 6 seconds and 10 seconds per alignment on CMU and Aachen, respectively, on a single NVIDIA A4000. |
| Software Dependencies | No | The paper states 'We implement our approach in Py Torch (Paszke et al., 2019)' but does not specify the version number of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We initialize p( ) with a truncated uniform distribution of a fixed radius around all interest points. The radius decreases from 50% of the image diagonal to 5% in the first 30 iterations. Afterward, the scheduler switches to the normal distribution around each interest point with standard deviation σ. Initially, we define σ such that 99% of the distribution covers 10% of the image around the point, and we decrease the coverage ratio to 1%. In our experiments with Super Point, we set the threshold value to 0.4. |