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Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes

Johanna Wald, Torsten Sattler, Stuart Golodetz, Tommaso Cavallari, Federico Tombari

arXiv:2008.02004v1cs.CV

TL;DR

Indoor re-localization benchmarks have paid less attention to realistic appearance and scene changes than outdoor benchmarks. This paper adapts 3RScan into RIO10 and evaluates methods with new performance and change metrics; it finds that long-term indoor re-localization remains unsolved.

  • Problem

    Existing indoor benchmarks focus on static scenes or do not quantify how indoor scene changes affect re-localization, despite such changes being common in real-world environments.

    Source: §1 · pp. 1–2

  • Method

    The paper adapts 3RScan into the RIO10 benchmark and introduces metrics for re-localization performance and indoor scene changes.

    Source: §1 · pp. 2–3, §3 · p. 6, Dense Correspondence Re-Projection… · p. 9

  • Results

    The experiments show that long-term indoor re-localization remains unsolved, and that many methods lose performance on visually, geometrically, and semantically changed regions.

    Source: §6 · p. 14

  • Takeaways & Limitations

    RIO10 and its evaluation tools provide a benchmark for measuring change impacts on indoor camera re-localization.

    Source: §1 · p. 3, §6 · p. 14

    Sequence-based experiments use perfect relative poses, eliminating tracking errors and therefore representing an upper bound on performance.

    Source: §7.5 · p. 19

Abstract

from arXiv · show

Long-term camera re-localization is an important task with numerous computer vision and robotics applications. Whilst various outdoor benchmarks exist that target lighting, weather and seasonal changes, far less attention has been paid to appearance changes that occur indoors. This has led to a mismatch between popular indoor benchmarks, which focus on static scenes, and indoor environments that are of interest for many real-world applications. In this paper, we adapt 3RScan - a recently introduced indoor RGB-D dataset designed for object instance re-localization - to create RIO10, a new long-term camera re-localization benchmark focused on indoor scenes. We propose new metrics for evaluating camera re-localization and explore how state-of-the-art camera re-localizers perform according to these metrics. We also examine in detail how different types of scene change affect the performance of different methods, based on novel ways of detecting such changes in a given RGB-D frame. Our results clearly show that long-term indoor re-localization is an unsolved problem. Our benchmark and tools are publicly available at waldjohannau.github.io/RIO10

1 Introduction

The paper addresses the lack of indoor benchmarks that quantify how realistic scene changes affect re-localization. It introduces a 3RScan-based benchmark and change-evaluation framework, and reports that real-world indoor re-localization remains unsolved.

1 Introduction

  • Existing indoor datasets do not quantify how realistic everyday scene changes affect re-localization performance.The paper describes indoor changes from human interaction as diverse and harder to predict than the cyclic changes commonly modeled outdoors.Source: §1 · p. 2
  • The benchmark adapts 3RScan, whose long-term everyday scans depict changes not captured by other datasets.It is designed to cover appearance, geometric, and semantic changes.Source: §1 · pp. 2–3
  • The framework enables quantitative measurement of how different change types affect predicted camera-pose accuracy.The paper presents this as the first such measurement to the authors’ knowledge.Source: §1 · p. 3
  • Experiments show that real-world indoor re-localization remains far from solved, and the benchmark, framework, and protocols are publicly available.The evaluation considers state-of-the-art methods for static indoor and changing outdoor scenes.Source: §1 · p. 3

2 Related Work

Prior re-localization benchmarks address outdoor change more directly than realistic indoor change, while indoor datasets either focus on static scenes or lack change-impact measurement. The paper situates its benchmark among several method families and evaluates selected state-of-the-art re-localizers.

2 Related Work

  • Outdoor benchmarks model lighting, weather, seasonal, and long-term geometric changes, while popular indoor datasets such as 7-Scenes and 12-Scenes are static.InLoc and NCLT contain moved-object changes but do not quantify their impact on re-localization performance.Source: §2 · p. 3
  • Re-localization approaches include image retrieval, direct pose regression, structure-based matching, semantic or object-based methods, and scene coordinate regression.Scene coordinate regression estimates query-image scene coordinates and uses them to generate and refine pose hypotheses.Source: §2 · pp. 4–5
  • Long-term localization methods use learned features, while semantic and object-based approaches face stated challenges in changing indoor scenes.Indoor objects may move, and one cited work argues that many outdoor semantic approaches are not directly applicable indoors.Source: §2 · pp. 4–5
  • The paper focuses on re-localizers with state-of-the-art performance on static indoor or dynamic outdoor scenes to study their response to scene change.The authors frame benchmark and metric development, rather than a new re-localizer, as the main contribution.Source: §2 · p. 5

3 Benchmark Dataset

RIO10 adapts a selected 10-scene subset of 3RScan for long-term indoor camera re-localization. Its scenes include varied settings and changes, with sequences, aligned models, and rendered evaluation data prepared for benchmarking.

3 Benchmark Dataset

  • The original 3RScan contains 1,482 scans of around 450 natural indoor environments, with globally aligned models reconstructed from RGB-D sequences.Camera poses are estimated using offline bundle adjustment based on fisheye images.Source: §3 · pp. 5–6
  • RIO10 is a 10-scene subset of 3RScan selected partly for scanning frequency and scene/change diversity; its scenes have among 3RScan’s highest time spans and scanning frequencies (5–12 scans each).Source: §3 · p. 6
  • RIO10 has 10 training, 10 validation, and 54 test sequences, with test models and poses expressed in the reference frame.The benchmark provides rendered depth maps, ground-truth instance segmentations, evaluation tools, and per-frame statistics.Source: §3 · p. 6

4 Evaluating Re-Localization in Changing Indoor Scenes

The section presents measures for quantifying indoor scene change and evaluating camera re-localization. It introduces DCRE as an appearance-based performance measure alongside conventional absolute pose errors.

4.1 Quantifying Change in (Indoor) Scenes

4.2 Measuring Re-Localization Performance

  • Conventional evaluation computes absolute translation error in meters and orientation error in degrees, then assesses the fraction of poses within chosen thresholds.The paper notes that manually selected thresholds do not reflect scene appearance, while separate median errors need not describe the same frames.Source: §4.2 · pp. 8–9
  • DCRE measures the average image-space displacement of dense 3D points reprojected using ground-truth and predicted poses.It is normalized by the image diagonal and is described as correlating with visual perception while combining translation and angular error in one number.Source: Dense Correspondence Re-Projection… · pp. 9–10

5 Experiments

The experiments evaluate state-of-the-art re-localizers on RIO10 and examine frame difficulty, scene-change effects, and sequence-based localization. Performance correlates with scene change, cumulative DCRE distinguishes error behaviors, and using frame sequences significantly improves DCRE.

5.2 Re-Localization Performance

  • The evaluation covers 165,744 test images and compares structure-based, learned RGB/RGB-D, and image-retrieval methods using DCRE and absolute-pose measures.Table 3 also reports inlier/outlier ratios, median pose and DCRE errors, and the fraction of failures involving a moved object.Source: §5.2 · p. 11, Table 3 · p. 12
  • Cumulative DCRE distinguishes methods that return increasingly inaccurate poses from Active Search, which localizes fewer frames but avoids overly incorrect poses.Grove v2 and D2-Net are examples of the former behavior; Active Search’s plot plateaus, indicating failed rather than highly incorrect re-localizations.Source: §5.2 · p. 12, Figure 6 · p. 12
  • A clear correlation appears between increasing visual, geometric, and semantic scene changes and overall re-localization performance.Fig. 7 plots performance against visual change (ζv and ρv), geometric change (ζg), and semantic change (ζs).Source: §5.2 · p. 12, Figure 7 · p. 13
  • When a moved object dominates the view, camera pose becomes ambiguous and a method may localize against the object instead of the global scene.The paper reports the fraction of such cases among failures in Table 3.Source: Table 3 · p. 12, §5.2 · p. 13, Figure 8 · p. 13
  • Frame sequences significantly improve DCRE, with lengths of 10 and 30 modeling interactive use and up to 100 frames suiting less time-sensitive applications.The corresponding comparisons are shown in Fig. 9.Source: §5.2 · p. 13

6 Conclusion

The paper presents RIO10 and scene-change metrics, including DCRE, to evaluate long-term indoor camera re-localization. Results show that everyday indoor changes remain challenging; frame sequences improve performance but do not solve the benchmark.

6 Conclusion

  • RIO10 and metrics for quantifying indoor scene changes enable evaluation of their impact on re-localization, and DCRE measures re-localization performance.The paper reports that many methods lose performance under visual, geometric, and semantic changes, including rigid or non-rigid object movements.Source: §6 · p. 14
  • No evaluated state-of-the-art re-localizer fully handles everyday indoor changes, and using short image sequences improves performance but does not solve the benchmark.Large semantic changes, such as a large object changing position, are particularly problematic when the object dominates the camera’s view.Source: §6 · p. 14
  • The paper argues that long-term indoor re-localization requires learning higher-level scene concepts, including semantics or object-level poses, dynamics, and appearance variations.Such understanding could support reasoning about scene changes and couple pose estimation more closely to scene understanding.Source: §6 · p. 14

7 Supplementary Material

The supplementary material documents RIO10’s scene diversity and change statistics, explains its frame-difficulty measures and DCRE metric, and provides implementation details for evaluated methods.

7.1 Benchmark Visualization

  • RIO10 contains 10 training, 10 validation, and 54 test sequences, with 52,562, 34,415, and 165,744 images in those sets, respectively.Its scenes include rigid object movements and non-rigid changes such as rearranged blankets.Source: §7.1 · p. 15
  • Figures 15–24 provide 3D reconstructions of the ten selected RIO10 scenes.The supplementary material also includes implementation details for HF-Net and image retrieval methods.Source: §7.1 · p. 15, §7 · p. 15

7.2 Change Statistics

  • Scene 4 has the highest semantic and geometric change, while scenes 8 and 9 show low normalized correlation and high normalized SSD.The per-scene statistics are computed from change measures averaged over test frames and sequences.Source: §7.2 · p. 15, Figure 10 · p. 16

7.3 Classifying Frame Difficulty

  • Low VoL indicates blur or little texture; higher DCRE broadly indicates a more novel test pose, and high-context frames were harder than medium-context frames on average.Low-context frames also challenged methods; high-context difficulty may reflect blur, texture scarcity, or large scene changes.Source: §7.3 · pp. 15–16

7.4 Dense Correspondence Re-Projection Error

  • DCRE measures dense reprojection error using ground-truth correspondences, is zero at the ground-truth pose, and can be computed across the image using rendered mesh point clouds.Its normalization is intended to help compare errors across datasets with different image resolutions.Source: §7.4 · pp. 17–18

7.5 Implementation Details

  • Sequence-based experiments use ground-truth relative poses, eliminating pose-tracking errors and making their results an upper bound; closing the gap to odometry or SLAM remains open.For RGB-only methods, sequences are modeled as generalized cameras and solved with gPnP+s inside RANSAC; RGB-D per-frame poses are transformed and clustered, with the largest cluster’s centroid returned.Source: §7.5 · pp. 18–19
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