3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)

• URL: http://arxiv.org/abs/2511.06300v1
• Date: Sun, 09 Nov 2025 09:35:45 GMT
• Title: 3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)
• Authors: Bar Genossar, Sagi Dalyot, Roee Shraga, Avigdor Gal,
• Abstract summary: 3dSAGER is an end-to-end pipeline for geospatial entity resolution over 3D objects.<n>We present a novel, spatial-reference-independent featurization mechanism that captures intricate geometric characteristics of matching pairs.<n>We also propose a new lightweight and interpretable blocking method, BKAFI, that leverages a trained model to efficiently generate high-recall candidate sets.
• Score: 7.378893412842889
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Urban environments are continuously mapped and modeled by various data collection platforms, including satellites, unmanned aerial vehicles and street cameras. The growing availability of 3D geospatial data from multiple modalities has introduced new opportunities and challenges for integrating spatial knowledge at scale, particularly in high-impact domains such as urban planning and rapid disaster management. Geospatial entity resolution is the task of identifying matching spatial objects across different datasets, often collected independently under varying conditions. Existing approaches typically rely on spatial proximity, textual metadata, or external identifiers to determine correspondence. While useful, these signals are often unavailable, unreliable, or misaligned, especially in cross-source scenarios. To address these limitations, we shift the focus to the intrinsic geometry of 3D spatial objects and present 3dSAGER (3D Spatial-Aware Geospatial Entity Resolution), an end-to-end pipeline for geospatial entity resolution over 3D objects. 3dSAGER introduces a novel, spatial-reference-independent featurization mechanism that captures intricate geometric characteristics of matching pairs, enabling robust comparison even across datasets with incompatible coordinate systems where traditional spatial methods fail. As a key component of 3dSAGER, we also propose a new lightweight and interpretable blocking method, BKAFI, that leverages a trained model to efficiently generate high-recall candidate sets. We validate 3dSAGER through extensive experiments on real-world urban datasets, demonstrating significant gains in both accuracy and efficiency over strong baselines. Our empirical study further dissects the contributions of each component, providing insights into their impact and the overall design choices.

Related papers

• SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs [21.891285551179365]
  We introduce Spherical Coordinate-based Positional Embedding (SoPE)<n>Our method maps point-cloud token indices into a 3D spherical coordinate space, enabling unified modeling of spatial locations and directional angles.<n>This formulation preserves the inherent geometric structure of point-cloud data, enhances spatial awareness, and yields more consistent and expressive geometric representations for multimodal learning.
  arXiv  Detail & Related papers  (2026-02-26T07:42:15Z)
• Collaborative Perceiver: Elevating Vision-based 3D Object Detection via Local Density-Aware Spatial Occupancy [7.570294108494611]
  Vision-based bird's-eye-view (BEV) 3D object detection has advanced significantly in autonomous driving.<n>Existing methods often construct 3D BEV representations by collapsing extracted object features.<n>We introduce a multi-task learning framework, Collaborative Perceiver, to bridge gaps in spatial representations and feature refinement.
  arXiv  Detail & Related papers  (2025-07-28T21:56:43Z)
• Towards Scalable Spatial Intelligence via 2D-to-3D Data Lifting [64.64738535860351]
  We present a scalable pipeline that converts single-view images into comprehensive, scale- and appearance-realistic 3D representations.<n>Our method bridges the gap between the vast repository of imagery and the increasing demand for spatial scene understanding.<n>By automatically generating authentic, scale-aware 3D data from images, we significantly reduce data collection costs and open new avenues for advancing spatial intelligence.
  arXiv  Detail & Related papers  (2025-07-24T14:53:26Z)
• Boosting Multi-View Indoor 3D Object Detection via Adaptive 3D Volume Construction [10.569056109735735]
  This work presents SGCDet, a novel multi-view indoor 3D object detection framework based on adaptive 3D volume construction.<n>We introduce a geometry and context aware aggregation module to integrate geometric and contextual information within adaptive regions in each image.<n>We show that SGCDet achieves state-of-the-art performance on the ScanNet, ScanNet200 and ARKitScenes datasets.
  arXiv  Detail & Related papers  (2025-07-24T11:58:01Z)
• Cross-Modal Geometric Hierarchy Fusion: An Implicit-Submap Driven Framework for Resilient 3D Place Recognition [9.411542547451193]
  We propose a novel framework that redefines 3D place recognition through density-agnostic geometric reasoning.<n>Specifically, we introduce an implicit 3D representation based on elastic points, which is immune to the interference of original scene point cloud density.<n>With the aid of these two types of information, we obtain descriptors that fuse geometric information from both bird's-eye view and 3D segment perspectives.
  arXiv  Detail & Related papers  (2025-06-17T07:04:07Z)
• Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data [14.104497777255137]
  We introduce Low-rank Efficient Spatial-Spectral Vision Transformer with three key innovations.<n>We pretrain LESS ViT using a Hyperspectral Masked Autoencoder framework with integrated positional and channel masking strategies.<n> Experimental results demonstrate that our proposed method achieves competitive performance against state-of-the-art multi-modal geospatial foundation models.
  arXiv  Detail & Related papers  (2025-03-17T05:42:19Z)
• GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding [53.42728468191711]
  Open-Vocabulary 3D object affordance grounding aims to anticipate action possibilities'' regions on 3D objects with arbitrary instructions.<n>We propose GREAT (GeometRy-intEntion collAboraTive inference) for Open-Vocabulary 3D Object Affordance Grounding.
  arXiv  Detail & Related papers  (2024-11-29T11:23:15Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image [94.56927147492738]
  We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes from single images. We show that leveraging diffusion priors can markedly improve generalization, detail preservation, and efficiency in resource usage. We propose a simple yet effective strategy to segregate the complex data distribution of various scenes into distinct sub-distributions.
  arXiv  Detail & Related papers  (2024-03-18T17:50:41Z)
• Towards Unified 3D Object Detection via Algorithm and Data Unification [70.27631528933482]
  We build the first unified multi-modal 3D object detection benchmark MM- Omni3D and extend the aforementioned monocular detector to its multi-modal version. We name the designed monocular and multi-modal detectors as UniMODE and MM-UniMODE, respectively.
  arXiv  Detail & Related papers  (2024-02-28T18:59:31Z)
• Neural Progressive Meshes [54.52990060976026]
  We propose a method to transmit 3D meshes with a shared learned generative space. We learn this space using a subdivision-based encoder-decoder architecture trained in advance on a large collection of surfaces. We evaluate our method on a diverse set of complex 3D shapes and demonstrate that it outperforms baselines in terms of compression ratio and reconstruction quality.
  arXiv  Detail & Related papers  (2023-08-10T17:58:02Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)

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.