InLoc Dataset Test Score Discrepancy: RoMa-Indoor Vs. Paper

Introduction to the InLoc Dataset and RoMa-Indoor

The InLoc dataset stands as a crucial benchmark in the field of visual localization, challenging systems to accurately determine their pose within a known environment using image data. This task is fundamental for various applications, from augmented reality and robotics to autonomous navigation. When RoMa-Indoor, a method based on publicly available weights, was integrated into the hloc framework and tested using the pipeline_inloc.ipynb script, the results obtained presented a notable deviation from the scores reported in the original paper. Specifically, the implemented pipeline yielded scores of 58.6 / 73.7 / 82.8 and 54.2 / 68.7 / 77.1, which are considerably lower than the paper's reported figures of 60.6 / 79.3 / 89.9 and 66.4 / 83.2 / 87.8. This significant difference raises important questions about the methodology, parameter settings, or potential misinterpretations during the integration and testing process. This article aims to delve into the user's approach, explore potential reasons for the discrepancy, and offer guidance on how to achieve results closer to the reported benchmarks, ensuring a more accurate and reliable evaluation of visual localization capabilities. We will systematically examine each step of the user's process, from feature matching to localization, and discuss the impact of various configuration choices on the final performance metrics. The goal is to provide a clear and comprehensive understanding of how to effectively reproduce and interpret the results from the InLoc dataset when using methods like RoMa-Indoor.

Detailed Breakdown of the User's Approach

To effectively troubleshoot the InLoc dataset test score discrepancy, it's essential to meticulously examine each step taken by the user. The user's process involved three primary stages: creating a RoMa matcher, registering the configuration, and executing the matching and localization pipeline. Let's break down each of these in detail. Firstly, the creation of a RoMa matcher was performed by adapting the BaseModel to handle tensor-scale input. The code snippet provided shows the process of taking image tensors (image0, image1), determining their dimensions (h1, w1, h2, w2), and then feeding them into the RoMa network for matching. The self.net.match function generates dense matches and their corresponding certainty scores, which are then sampled using self.net.sample to obtain a sparse set of matches along with their certainty. Finally, self.net.to_pixel_coordinates converts these sparse matches into pixel coordinates (kpts0, kpts1). The output is structured in the HLoc format, returning keypoints and scores. This part of the process seems to align with the general requirements of feature extraction and matching for localization tasks. The second stage involved registering the configuration in match_dense.py. A specific configuration for roma_indoor was defined, including parameters like output, model, preprocessing options, max_error, and cell_size. Notably, the preprocessing section specifies grayscale: false, which is highlighted as critical for passing RGB data, and resize_max: 1344, along with dfactor: 14. These parameters are vital as they dictate how the input images are processed before being fed into the RoMa model, influencing the scale and features that the model operates on. The third stage was the execution of matching and localization in pipeline_inloc.ipynb. Here, the dense_conf for roma_indoor was loaded, and match_dense.main was called to generate features and matches from the localization pairs. Subsequently, localize_inloc.main was used to perform the actual localization using the generated features and matches, with the skip_matches parameter set to 20. The RANSAC settings for the estimation phase were also specified: ransac.max_error = 50, ransac.confidence = 0.99999, and ransac.min_inlier_ratio = 0.1. These settings govern how robustly a pose estimate is derived from the feature matches, filtering out outliers and ensuring a high-confidence solution. The visualization results, obtained through visualization.visualize_loc, appear reasonably good, suggesting that the core localization process is functional, but the quantitative scores are lagging behind expectations. Understanding the interplay of these settings is key to diagnosing the performance gap.

Potential Causes for the Score Discrepancy

The significant difference between the reported InLoc dataset test scores and those obtained by the user, despite a seemingly sound methodology, can stem from several subtle yet critical factors. One primary area to investigate is the exact implementation of the RoMa matcher within the hloc framework. While the user has provided code snippets, minor deviations in how the BaseModel is adapted, especially concerning tensor dimensions, normalization, or the specific sample function's behavior (e.g., the num parameter for max_num_matches), could lead to variations in the extracted features and matches. The sample function, in particular, determines which matches are considered 'sparse' and passed to the localization stage. If the sampling strategy or the number of matches selected differs from the paper's experimental setup, it could directly impact the localization accuracy. Another crucial aspect is the preprocessing configuration. The user correctly noted that grayscale: false is critical for RGB input. However, other preprocessing steps like image resizing (resize_max) and the dfactor (downscaling factor) can have a substantial impact. If the dfactor in the user's setup is not precisely what was used in the paper (e.g., if dfactor=14 implies a specific type of downsampling or if it interacts differently with resize_max), it could lead to features being extracted at a different effective resolution. Parameter tuning for the localization pipeline is another major suspect. While the RANSAC parameters (max_error, confidence, min_inlier_ratio) are set to robust values, it's possible that the optimal RANSAC parameters for this specific RoMa-hloc integration are different from standard or assumed values. Furthermore, the skip_matches parameter in localize_inloc.main determines the minimum number of matches required to attempt localization. If this value is too high or too low relative to the number of matches actually produced by RoMa, it could prematurely discard potential localization attempts or lead to unstable estimates. The underlying weights of the RoMa-Indoor model themselves could also be a factor. If the publicly available weights were trained with slightly different preprocessing or on a subtly different dataset distribution, they might not perform optimally on the InLoc dataset as expected. It's also worth considering the dataset split and evaluation protocol. Are the training/validation/testing splits used by the user identical to those used in the paper? Subtle differences in how the dataset is partitioned can lead to performance variations. Finally, numerical precision or floating-point differences between different hardware or software environments, though less common, can sometimes contribute to minor score discrepancies, especially in deep learning pipelines. Evaluating these points systematically is key to pinpointing the exact source of the performance gap.

Recommendations for Reproducing Reported Scores

To bridge the gap and achieve InLoc dataset test scores that are closer to the reported benchmarks, several key areas should be revisited and fine-tuned. Firstly, it is highly recommended to verify the exact preprocessing steps used in the original paper for RoMa-Indoor. This includes not only the grayscale setting and resize_max but also the specific interpolation method used for resizing and any normalization applied to the image pixels before they are fed into the RoMa model. Small variations in these steps can significantly alter the feature representations. Secondly, thoroughly investigate the sample function's parameters. The num=self.max_num_matches in the user's code is a crucial point. The paper likely specifies a particular value for max_num_matches or a specific strategy for selecting sparse matches (e.g., based on certainty thresholds). Experimenting with different values for max_num_matches and understanding how sparse_certainty is used to filter matches is essential. It's possible that the number of sampled matches is either too high, leading to more noise, or too low, missing critical inliers. Thirdly, re-evaluate the RANSAC parameters in the context of the matches produced by RoMa-Indoor. While the current settings are robust, they might be overly strict or too lenient for the specific type and distribution of matches generated by RoMa. Consider running a sweep of max_error values (e.g., between 10 and 100 pixels) and min_inlier_ratio (e.g., between 0.05 and 0.2) to find the optimal configuration that maximizes the localization recall while maintaining high precision. The skip_matches parameter in localize_inloc.main should also be adjusted. If RoMa consistently produces fewer matches than 20, localization will never be attempted. Try reducing this threshold (e.g., to 10 or even lower, depending on dataset characteristics) to enable more localization attempts and assess the resulting scores. Furthermore, confirm the exact version and source of the RoMa-Indoor weights. Ensure that the weights used are precisely those intended for the InLoc dataset or a similar indoor environment, and that no unintended modifications or conversions have occurred. If possible, try to obtain the original codebase or configuration files used in the paper to ensure feature extraction and matching are as identical as possible. Lastly, consider the evaluation metric details. Ensure that the user's script correctly calculates the recall@5cm/10deg, recall@10cm/20deg, and recall@20cm/40deg metrics, as defined by the InLoc dataset. Small off-by-one errors or incorrect threshold comparisons in the scoring script could lead to perceived discrepancies. By systematically addressing these points, the user can increase the likelihood of reproducing the reported performance of RoMa-Indoor on the InLoc dataset.

Conclusion

Reproducing benchmark scores in computer vision research can often be a complex endeavor, and the InLoc dataset test score discrepancy observed with RoMa-Indoor highlights this challenge. The user's detailed approach, involving the creation of a custom matcher, configuration registration, and execution of a localization pipeline, provides a solid foundation for debugging. However, as explored, subtle differences in preprocessing, match sampling strategies, RANSAC parameter tuning, and even the specific weights or dataset splits can lead to significant performance variations. The visualization showing