Geometric Consistency Protocol for Foundation Model Features in Multi-View Satellite Imagery

• URL: http://arxiv.org/abs/2606.17564v1
• Date: Tue, 16 Jun 2026 06:09:08 GMT
• Title: Geometric Consistency Protocol for Foundation Model Features in Multi-View Satellite Imagery
• Abstract summary: We propose a geometry-faithful and reproducible protocol tailored for the Rational Polynomial Coefficients (RPC) framework.<n>We show that state-of-the-art 2D backbones remain remarkably competitive against specialized 3D-aware models when subjected to this RPC-consistent evaluation.
• Score: 11.56882902562207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Standardized evaluation protocols are indispensable for robust benchmarking in remote sensing, particularly as foundation features are increasingly transferred across diverse sensors and complex imaging geometries. In satellite multi-view reconstruction, conventional evaluations relying on unconstrained 2D global matching are often misleading. The Rational Function Model (RFM) and its Rational Polynomial Coefficients (RPC) dictate a curved, height-dependent epipolar geometry that render flat 2D search spaces physically inconsistent. We propose a geometry-faithful and reproducible protocol tailored for the RPC framework. Our approach integrates an RPC-projected 3D consistency metric with a geometry-constrained dense matching proxy, specifically evaluating whether similarity responses remain localized and unique under physically plausible search manifolds. A pivotal finding of our joint reporting strategy is the decoupling of semantic agreement and geometric localization: high cross-view similarity at a projected 3D point does not guarantee reliable matchability in practical inference. Our benchmark demonstrates that incorporating geometric constraints is fundamental to the problem definition in satellite imagery. Furthermore, we show that state-of-the-art 2D backbones remain remarkably competitive against specialized 3D-aware models when subjected to this RPC-consistent evaluation.

Related papers

• GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment [32.33973839948303]
  Multi-view anomaly detection (MvAD) detects defects by exploiting complementary observations from multiple camera viewpoints.<n>Existing methods typically fall into two extremes: voxel-based fusion provides explicit geometric alignment but requires costly 3D construction and class-specific assumptions.<n>We propose GeoMAD, a unified multi-view, multi-class AD framework that addresses both geometric correspondence deficiency and distributional inconsistency.
  arXiv  Detail & Related papers  (2026-08-27T07:15:15Z)
• Reliability-Aware 3D Geometric Injection for Universal Person Re-identification [47.874819213762926]
  We propose a Universal Monocular 3D-Enhanced ReID framework driven by a Consistency-Aware Reliability Gate and Dual-Stream Residual Fusion.<n>Our method improves challenging, structure-sensitive scenarios while preserving competitive performance on clean domains.
  arXiv  Detail & Related papers  (2026-07-21T08:54:25Z)
• G$^2$TAM: Geometry Grounded Track Anything Model [49.05553245076823]
  We propose a unified framework for promptable instance tracking in 3D using only unordered RGB images or videos.<n>G$2$TAM employs spatially aligned geometric representations as implicit memory, ensuring stable instance identity and localization across frames and views.<n>At its core is a cross-modal spatial encoder that integrates visual and textual prompts into a shared geometric space, enabling end-to-end spatial reconstruction and instance-consistent mask prediction.
  arXiv  Detail & Related papers  (2026-07-04T09:32:13Z)
• EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction [14.023396396575132]
  We present EO-VGGT, a framework that adapts a frozen perspective-driven model to orbital observations via explicit physical geometry embedding.<n>Our findings underscore that integrating explicit physical geometry with optimized view selection is essential for robust feed-forward satellite 3D reconstruction.
  arXiv  Detail & Related papers  (2026-07-01T04:19:49Z)
• PASR: Pose-Aware 3D Shape Retrieval from Occluded Single Views [41.6619757077912]
  We propose Pose-Aware 3D Shape Retrieval (PASR), a framework that formulates retrieval as a feature-level analysis-by-language problem.<n>By aligning pose-conditioned 3D projections with 2D feature maps, our method bridges the gap between real-world images and synthetic meshes.<n>PASR substantially outperforms existing methods on both clean and occluded 3D shape retrieval datasets by a wide margin.
  arXiv  Detail & Related papers  (2026-04-24T15:32:03Z)
• URoPE: Universal Relative Position Embedding across Geometric Spaces [55.651792747815854]
  URoPE is a universal extension of Rotary Position Embedding (RoPE) to cross-view or cross-dimensional geometric spaces.<n>For each key/value image patch, URoPE samples 3D points along the corresponding camera ray at predefined depth anchors.<n>Standard 2D RoPE can then be applied using the projected pixel coordinates.
  arXiv  Detail & Related papers  (2026-04-20T18:52:03Z)
• LG-HCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting [77.81227097905865]
  Anchor-based 3DGS compression schemes reduce gaussian redundancy through some advanced context models.<n>We propose a Local Geometry-aware Hierarchical Context Compression framework for 3DGS that incorporates inter-anchor geometric correlations into anchor pruning and entropy coding for compact representation.<n>Experiments show that LG-HCC effectively alleviates structural preservation issues,achieving superior geometric integrity and rendering fidelity while reducing storage by up to 30.85x compared to the Scaffold-GS baseline on the Mip-NeRF360 dataset.
  arXiv  Detail & Related papers  (2026-03-30T13:39:35Z)
• UniQueR: Unified Query-based Feedforward 3D Reconstruction [68.01984215138098]
  We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images.<n>Our model learns a compact set of 3D anchor points that act as explicit geometric queries.<n>Experiments on Mip-NeRF 360 and VR-NeRF demonstrate that UniQueR surpasses state-of-the-art feedforward methods in both rendering quality and geometric accuracy.
  arXiv  Detail & Related papers  (2026-03-24T06:42:02Z)
• GGPT: Geometry Grounded Point Transformer [22.64445696362087]
  We introduce the Geometry-Grounded Point Transformer (GGPT), a framework that augments feed-forward reconstruction with reliable sparse geometric guidance.<n>Building on this foundation, we propose a geometry-guided 3D point transformer that refines dense point maps under explicit partial-geometry supervision.
  arXiv  Detail & Related papers  (2026-03-11T18:00:04Z)
• Geometry OR Tracker: Universal Geometric Operating Room Tracking [61.399734016038614]
  In operating rooms (OR), world-scale multi-view 3D tracking supports downstream applications such as surgeon behavior recognition.<n>Camera calibration and RGB-D registration are always unreliable, leading to cross-view geometric inconsistency.<n>We introduce Geometry OR Tracker, a two-stage pipeline that rectifies imprecise calibration into a scaleconsistent and geometrically consistent camera setup.
  arXiv  Detail & Related papers  (2026-02-28T09:21:21Z)
• Robust Mesh Saliency GT Acquisition in VR via View Cone Sampling and Geometric Smoothing [59.12032628787018]
  3D mesh saliency ground truth is essential for human-centric visual modeling in virtual reality (VR)<n>Current VR eye-tracking pipelines rely on single ray sampling and Euclidean smoothing, triggering texture attention and signal leakage across gaps.<n>This paper proposes a robust framework to address these limitations.
  arXiv  Detail & Related papers  (2026-01-06T05:20:12Z)
• TALO: Pushing 3D Vision Foundation Models Towards Globally Consistent Online Reconstruction [57.46712611558817]
  3D vision foundation models have shown strong generalization in reconstructing key 3D attributes from uncalibrated images through a single feed-forward pass.<n>Recent strategies align consecutive predictions by solving global transformation, yet our analysis reveals their fundamental limitations in assumption validity, local alignment scope, and robustness under noisy geometry.<n>We propose a higher-DOF and long-term alignment framework based on Thin Plate Spline, leveraging globally propagated control points to correct spatially varying inconsistencies.
  arXiv  Detail & Related papers  (2025-12-02T02:22:20Z)
• SPIDER: Spatial Image CorresponDence Estimator for Robust Calibration [28.539636818320886]
  We introduce SPIDER, a universal feature matching framework that integrates a shared feature extraction backbone with two specialized network heads.<n> SPIDER significantly outperforms SoTA methods, demonstrating its strong ability as a universal image-matching method.
  arXiv  Detail & Related papers  (2025-11-21T20:02:08Z)
• Dens3R: A Foundation Model for 3D Geometry Prediction [44.13431776180547]
  Dens3R is a 3D foundation model designed for joint geometric dense prediction.<n>By integrating image-pair matching features with intrinsic invariance modeling, Dens3R accurately regresses multiple geometric quantities.
  arXiv  Detail & Related papers  (2025-07-22T07:22:30Z)

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.