From Uncertainty to Determinism: Coarse-to-Fine Visual Floorplan Localization without Ray Matching

• URL: http://arxiv.org/abs/2607.26817v2
• Date: Sun, 02 Aug 2026 12:33:26 GMT
• Title: From Uncertainty to Determinism: Coarse-to-Fine Visual Floorplan Localization without Ray Matching
• Abstract summary: We propose a coarse-to-fine visual FLoc framework that progresses from uncertainty to determinism.<n>In the refinement stage, we propose a localized refiner that predicts bounded sub-meter pose residuals from candidate-centered crops.<n>Our method effectively balances global multi-hypothesis tracking and local sub-meter refinement without requiring any offline map preprocessing or test-time lookup tables.
• Score: 12.273420964874058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Floorplan Localization (FLoc) has emerged as a promising solution for indoor localization by matching egocentric images against minimalist structural maps. However, due to cross-modal information asymmetry and repetitive indoor layouts, visual FLoc is fundamentally challenged by multimodal pose distributions, where visually identical observations map to distinct, spatially separated locations. Existing ray-matching-based methods tackle this by explicitly predicting sparse geometric or semantic rays, which inherently incur information loss and demand resource-intensive preprocessing alongside exhaustive matching during inference. In this paper, we bypass the intermediate ray-matching paradigm and propose a coarse-to-fine visual FLoc framework that progresses from uncertainty to determinism. In the coarse stage, we design an image-conditioned pose diffusion model to parameterize the continuous multimodal pose distribution, effectively routing stochastically initialized pose particles toward distinct candidate modes. In the refinement stage, we propose a localized refiner that predicts bounded sub-meter pose residuals from candidate-centered floorplan crops, where structural ambiguities are largely eliminated. Our method effectively balances global multi-hypothesis tracking and local sub-meter refinement without requiring any offline map preprocessing or test-time lookup tables. Comprehensive results on the S3D (full) and ZInD benchmarks demonstrate that our approach achieves state-of-the-art accuracy and robustness.

Related papers

• Adaptive Depth-Map-Guided Bundle Adjustment for Correspondence-Free Multi-View Point Cloud Registration [10.891615423471317]
  This paper presents an adaptive layered depth-map-guided bundle adjustment framework for correspondence-free multi-view point cloud registration.<n> Experiments on self-collected industrial datasets show that the proposed method achieves consistently competitive reconstruction accuracy.
  arXiv  Detail & Related papers  (2026-09-01T11:28:46Z)
• Topometric Autonomous Vehicle Localization by Combining Visual Embeddings and Feed-Forward 3D Models [3.807858988066276]
  We propose a framework for visual localization that combines VPR with metric pose estimation in controlled image sets.<n>We extensively evaluate the framework on three known benchmarks, demonstrating substantial improvements over existing appearance-based methods.<n>The modularity of our approach allows the descriptor extractor and FF3D model to remain interchangeable, and a focused analysis shows that sequential belief can severe failures under perceptual aliasing.
  arXiv  Detail & Related papers  (2026-08-06T13:26:47Z)
• LeCropFollow: Latent Space Planning for Navigation in Unstructured Crop Fields [7.898784066160524]
  We present LeCropFollow, a visual navigation framework that bypasses explicit geometric modeling in favor of a learned latent representation.<n>We show that LeCropFollow matches state-of-the-art baselines in unstructured rows but significantly outperforms them in plantation gaps.<n>These results suggest that latent planning offers a robust alternative to geometric estimation for operations in heterogeneous agricultural environments.
  arXiv  Detail & Related papers  (2026-06-30T16:47:08Z)
• Information-Preserving Continuous Occupancy Mapping with Variance-Weighted Submap Joining [10.891615423471317]
  We propose the first continuous submap joining framework that jointly optimize submap poses and a global occupancy field in the latent log-odds space.<n>Experiments on both simulated and large-scale real-world datasets demonstrate that the proposed method higher pose accuracy and improved global consistency than state-of-the-art grid-based submap joining approaches.
  arXiv  Detail & Related papers  (2026-06-09T05:38:11Z)
• Geometry-Aware Dataset Condensation for Diffusion Model Training [103.45641113998839]
  We propose to reformulate real subset selection as a geometry-aware distribution alignment problem.<n>By incorporating one-sided partial optimal transport, our method selectively aligns a compact subset with the full data distribution.<n>Experiments across diffusion variants, subset sizes, image resolutions, and training rounds show that our method achieves superior fidelity and distributional coverage.
  arXiv  Detail & Related papers  (2026-06-04T08:53:58Z)
• A Bayesian Approach for Task-Specific Next-Best-View Selection with Uncertain Geometry [50.782490644267334]
  We develop a framework for task-specific active next-best-view selection in 3D reconstruction from point clouds.<n>Our framework achieves superior task performance with fewer views compared to commonly used baselines and prior general uncertainty-reduction techniques.
  arXiv  Detail & Related papers  (2026-05-06T16:32:55Z)
• Entropy-Gradient Grounding: Training-Free Evidence Retrieval in Vision-Language Models [77.3748853516374]
  We propose a training-free, model-intrinsic grounding method for vision-language models.<n>We compute the entropy of the model's next-token distribution and backpropagate it to the visual token embeddings to obtain an entropy-gradient relevance map.<n>We then extract and rank multiple coherent regions to support multi-evidence queries, and introduce an iterative zoom-and-reground procedure with a spatial-entropy stopping rule.
  arXiv  Detail & Related papers  (2026-04-09T16:51:42Z)
• Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models [84.78794648147608]
  A persistent geometric anomaly, the Modality Gap, remains.<n>Prior approaches to bridge this gap are largely limited by oversimplified isotropic assumptions.<n>We propose the Fixed-frame Modality Gap Theory, which decomposes the modality gap into stable biases and anisotropic residuals.<n>We then introduce ReAlign, a training-free modality alignment strategy.
  arXiv  Detail & Related papers  (2026-02-02T13:59:39Z)
• Non-Rigid Structure-from-Motion via Differential Geometry with Recoverable Conformal Scale [17.935227965480475]
  We introduce a novel method, called Con-NRSfM, for NRSfM under conformal deformations.<n>Our approach performs point-wise reconstruction using 2D selected image warps optimized through a graph-based framework.<n>Our framework decouples constraints on depth and conformal scale, which are inseparable in other approaches.
  arXiv  Detail & Related papers  (2025-10-02T04:46:46Z)
• Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation [70.32536356351706]
  We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations. We derive suitable measures to quantify prediction uncertainty at both pose and joint level. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-29T07:14:58Z)
• SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings [2.534402217750793]
  We present an approach to learn dense, continuous 2D-3D correspondence distributions over the surface of objects from data. We also present a new method for 6D pose estimation of rigid objects using the learnt distributions to sample, score and refine pose hypotheses.
  arXiv  Detail & Related papers  (2021-11-26T13:39:38Z)
• 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)

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.