UnLoc: Leveraging Depth Uncertainties for Floorplan Localization

• URL: http://arxiv.org/abs/2509.11301v1
• Date: Sun, 14 Sep 2025 14:45:43 GMT
• Title: UnLoc: Leveraging Depth Uncertainties for Floorplan Localization
• Authors: Matthias Wüest, Francis Engelmann, Ondrej Miksik, Marc Pollefeys, Daniel Barath,
• Abstract summary: UnLoc is an efficient data-driven solution for sequential camera localization within floorplans.<n>We introduce a novel probabilistic model that incorporates uncertainty estimation, modeling depth predictions as explicit probability distributions.<n>We evaluate UnLoc on large-scale synthetic and real-world datasets, demonstrating significant improvements in terms of accuracy and robustness.
• Score: 80.55849461031879
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose UnLoc, an efficient data-driven solution for sequential camera localization within floorplans. Floorplan data is readily available, long-term persistent, and robust to changes in visual appearance. We address key limitations of recent methods, such as the lack of uncertainty modeling in depth predictions and the necessity for custom depth networks trained for each environment. We introduce a novel probabilistic model that incorporates uncertainty estimation, modeling depth predictions as explicit probability distributions. By leveraging off-the-shelf pre-trained monocular depth models, we eliminate the need to rely on per-environment-trained depth networks, enhancing generalization to unseen spaces. We evaluate UnLoc on large-scale synthetic and real-world datasets, demonstrating significant improvements over existing methods in terms of accuracy and robustness. Notably, we achieve $2.7$ times higher localization recall on long sequences (100 frames) and $16.7$ times higher on short ones (15 frames) than the state of the art on the challenging LaMAR HGE dataset.

Related papers

• ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving [16.84661057744478]
  We introduce a large-scale, diverse, frame-wise continuous dataset for depth estimation in dynamic outdoor driving environments.<n>Compared to existing datasets, ours presents greater diversity in driving scenarios and lower depth density, creating new challenges for generalization.
  arXiv  Detail & Related papers  (2025-08-19T16:13:49Z)
• Propagating Sparse Depth via Depth Foundation Model for Out-of-Distribution Depth Completion [33.854696587141355]
  We propose a novel depth completion framework that leverages depth foundation models to attain remarkable robustness without large-scale training.<n>Specifically, we leverage a depth foundation model to extract environmental cues, including structural and semantic context, from RGB images to guide the propagation of sparse depth information into missing regions.<n>Our framework performs remarkably well in the OOD scenarios and outperforms existing state-of-the-art depth completion methods.
  arXiv  Detail & Related papers  (2025-08-07T02:38:24Z)
• Amodal Depth Anything: Amodal Depth Estimation in the Wild [39.27552294431748]
  Amodal depth estimation aims to predict the depth of occluded (invisible) parts of objects in a scene.<n>We propose a novel formulation of amodal depth estimation in the wild, focusing on relative depth prediction to improve model generalization across diverse natural images.<n>We present two complementary frameworks: Amodal-DAV2, a deterministic model based on Depth Anything V2, and Amodal-DepthFM, a generative model that integrates conditional flow matching principles.
  arXiv  Detail & Related papers  (2024-12-03T09:56:38Z)
• Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering [93.94371335579321]
  We propose a learning framework that trains models to predict geometry-preserving depth without requiring extra data or annotations. Comprehensive experiments underscore our framework's superior generalization capabilities. Our innovative loss functions empower the model to autonomously recover domain-specific scale-and-shift coefficients.
  arXiv  Detail & Related papers  (2023-09-18T12:36:39Z)
• SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes [58.89295356901823]
  Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions. We introduce an external pretrained monocular depth estimation model for generating single-image depth prior. Our model can predict sharp and accurate depth maps, even when training from monocular videos of highly-dynamic scenes.
  arXiv  Detail & Related papers  (2022-11-07T16:17:47Z)
• Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies [0.4523163728236143]
  We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning. The proposed cross-modal framework significantly outperforms deep learning-only predictions with respect to model scalability and tolerance to environmental variations.
  arXiv  Detail & Related papers  (2022-10-27T15:48:53Z)
• Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction [63.3021778885906]
  3D bounding boxes are a widespread intermediate representation in many computer vision applications. We propose methods for leveraging our autoregressive model to make high confidence predictions and meaningful uncertainty measures. We release a simulated dataset, COB-3D, which highlights new types of ambiguity that arise in real-world robotics applications.
  arXiv  Detail & Related papers  (2022-10-13T23:57:40Z)
• Smooth densities and generative modeling with unsupervised random forests [1.433758865948252]
  An important application for density estimators is synthetic data generation. We propose a new method based on unsupervised random forests for estimating smooth densities in arbitrary dimensions without parametric constraints. We prove the consistency of our approach and demonstrate its advantages over existing tree-based density estimators.
  arXiv  Detail & Related papers  (2022-05-19T09:50:25Z)
• PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
  Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences. We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-09-28T17:56:41Z)
• Bayesian Optimization Meets Laplace Approximation for Robotic Introspection [41.117361086267806]
  We introduce a scalable Laplace Approximation (LA) technique to make Deep Neural Networks (DNNs) more introspective. In particular, we propose a novel Bayesian Optimization (BO) algorithm to mitigate their tendency of under-fitting the true weight posterior. We show that the proposed framework can be scaled up to large datasets and architectures.
  arXiv  Detail & Related papers  (2020-10-30T09:28:10Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.