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 | Conference PDF | Archive PDF | Plain Text | 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. |