VGGT-CD: Training-Free Robust Registration for 3D Change Detection

• URL: http://arxiv.org/abs/2605.16859v1
• Date: Sat, 16 May 2026 07:44:04 GMT
• Title: VGGT-CD: Training-Free Robust Registration for 3D Change Detection
• Abstract summary: VGGT-CD is a training-free pipeline registration from dynamic-change interference.<n>It reduces Absolute Trajectory Error by 44% outdoors and 59% indoors.<n>It completes registration over 6 times faster, producing high-purity 3D change maps without task-specific training.
• Score: 10.370711482793135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D change detection from multi-view images is essential for urban monitoring, disaster assessment, and autonomous driving. However, existing methods predominantly operate in the 2D domain, where viewpoint variations are mistaken for physical changes and depth is unavailable. While visual geometry foundation models like VGGT rapidly produce dense point clouds from unposed images, independent per-epoch reconstruction encounters fundamental obstacles: unpredictable inter-epoch scale ambiguity, registration-change paradox where scene changes corrupt alignment, and pervasive edge-flying noise. To address these challenges, we present VGGT-CD, a training-free pipeline decoupling cross-temporal registration from dynamic-change interference. In the Coarse Stage, sparse keyframe joint inference establishes a unified metric space and yields an initial Sim(3) prior. In the Fine Stage, dense reconstructions are purified by isolating static-background correspondences. A closed-form centroid alignment refines the translation while locking scale and rotation, using a residual self-check to mathematically guarantee non-degradation. Evaluated on an 11-scene benchmark from the World Across Time dataset, VGGT-CD reduces Absolute Trajectory Error by 44% outdoors and 59% indoors. It completes registration over 6 times faster, producing high-purity 3D change maps without task-specific training.

Related papers

• VGGT-Align: Bridging Local Reconstruction and Global Consistency for Long-Sequence 3D Reconstruction [8.983872448985627]
  We present a scale-consistency enhancement framework built on a key insight: in structured environments such as driving scenes, geometric quantities remain inherently invariant across temporal segments.<n>We propose Scene Geometric Invariant Anchoring (SGIA), which extracts dominant geometric invariants from each chunk's predicted point cloud via coarse-to-fine robust estimation.<n> Experiments on multiple long-sequence benchmarks demonstrate state-of-the-art performance, reducing absolute trajectory error by up to 32% with significant gains in trajectory stability and reconstruction quality.
  arXiv  Detail & Related papers  (2026-08-15T14:32:55Z)
• RECO: Region-Aware Compensation for Extrinsic Perturbations in Roadside 3D Detection [13.05494342638977]
  Roadside 3D object detection provides wide-area perception crucial for traffic understanding, cooperative early warning, and safe autonomous driving.<n>Existing methods suffer from high sensitivity to camera extrinsics; even slight deviations can be amplified by projective geometry.<n>We propose RECO, a region-aware extrinsic compensation framework that corrects extrinsics using piecewise 6-DoF pose offsets.
  arXiv  Detail & Related papers  (2026-07-23T05:56:42Z)
• 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)
• Visual Geometry Transformer in the Wild: Distractor-Free 3D Reconstruction [77.75911117889915]
  Current end-to-end multi-view 3D reconstruction methods achieve impressive results, but rely on a restrictive static assumption.<n>This reliance on idealized inputs causes even the most advanced methods to fail in real-world settings.<n>We propose Visual Geometry Transformer in the Wild, an end-to-end framework for robust reconstruction from inconsistent views.
  arXiv  Detail & Related papers  (2026-06-22T02:52:22Z)
• SCOPE: Scale-Consistent One-Pass Estimation of 3D Geometry [69.89196162649091]
  SCOPE (Scale-Consistent One-Pass Estimation of 3D Geometry) is a novel approach for estimating 3D geometry from extended monocular video sequences.<n>Our approach generates affine-invariant 3D point maps with shared parameters across entire sequences, enabling consistent scale-invariant representations.
  arXiv  Detail & Related papers  (2026-06-19T10:23:25Z)
• VGGT-Edit: Feed-forward Native 3D Scene Editing with Residual Field Prediction [59.303842406260124]
  VGGT-Edit is a feed-forward framework for text-conditioned native 3D scene editing.<n>We introduce depth-synchronized text injection to align semantic guidance with the backbone's spatial poses.<n> VGGT-Edit substantially outperforms 2D-lifting baselines, producing sharper object details, stronger multi-view consistency, and near-instant inference speed.
  arXiv  Detail & Related papers  (2026-05-14T17:59:04Z)
• Think as Needed: Geometry-Driven Adaptive Perception for Autonomous Driving [4.320171553006484]
  Current 3D detection models apply a fixed computation budget to every frame, wasting resources on simple scenes while lacking capacity for complex ones.<n>We propose Enhanced HOPE, an adaptive perception architecture that measures the geometric complexity of each incoming LiDAR frame.<n>On the nuScenes and CARLA benchmarks, Enhanced HOPE reduces latency by 38% on simple scenes with no accuracy loss, and improves mean Average Precision by 2.7 points on rare long-tail scenarios.
  arXiv  Detail & Related papers  (2026-05-11T07:34:13Z)
• DPG-CD: Depth-Prior-Guided Cross-Modal Joint 2D-3D Change Detection [11.247504713156887]
  DPG-CD is a depth-prior-guided multi-temporal cross-modal fusion framework for joint 2D semantic and 3D height change detection.<n>A gated fusion mechanism then selectively injects geometric cues from depth prior while preserving discriminative spectral representations.<n>Experiments on two public datasets, Hi-BCD and 3DCD, and a new dataset, NYC-MMCD, demonstrate that DPG-CD outperforms state-of-the-art methods on both 2D and 3D change detection tasks.
  arXiv  Detail & Related papers  (2026-05-08T02:36:09Z)
• Unifying UAV Cross-View Geo-Localization via 3D Geometric Perception [51.687842983240564]
  Cross-view geo-localization for Unmanned Aerial Vehicles (UAVs) remains challenging due to the severe geometric discrepancy between oblique UAV imagery and satellite maps.<n>We propose a geometry-aware UAV geo-localization framework that explicitly models the 3D scene geometry to unify coarse place recognition and fine-grained pose estimation.<n>Our method significantly outperforms state-of-the-art baselines, achieving robust meter-level localization accuracy and improved generalization in complex urban environments.
  arXiv  Detail & Related papers  (2026-04-02T08:08:41Z)
• SpatialFly: Geometry-Guided Representation Alignment for UAV Vision-and-Language Navigation in Urban Environments [49.966170814478915]
  We propose a geometry-guided spatial representation framework for UAV VLN.<n>Running on RGB observations without explicit 3D reconstruction, SpatialFly introduces a geometry-guided 2D representation alignment mechanism.<n> Experimental results show that SpatialFly consistently outperforms state-of-the-art UAV VLN baselines across both seen and unseen environments.
  arXiv  Detail & Related papers  (2026-03-22T03:56:58Z)
• 3D Scene Change Modeling With Consistent Multi-View Aggregation [18.547603626073585]
  SCaR-3D is a novel 3D scene change detection framework that identifies object-level changes from a dense-view pre-change image sequence and sparse-view post-change images.<n>Our approach consists of a signed-distance-based 2D differencing module followed by multi-view aggregation with voting and pruning.<n>We also develop a continual scene reconstruction strategy that selectively updates dynamic regions while preserving the unchanged areas.
  arXiv  Detail & Related papers  (2025-12-28T08:00:56Z)
• DiffRegCD: Integrated Registration and Change Detection with Diffusion Features [74.3102451211493]
  We present DiffRegCD, an integrated framework that unifies dense registration and change detection in a single model.<n>Experiments on aerial (LEVIR-CD, DSIFN-CD, WHU-CD, SYSU-CD) and ground level (VL-CMU-CD) datasets show that DiffRegCD consistently surpasses recent baselines.
  arXiv  Detail & Related papers  (2025-11-11T07:32:19Z)
• RQR3D: Reparametrizing the regression targets for BEV-based 3D object detection [0.4604003661048266]
  Bird's-eye view (BEV)-based perception approaches have emerged as superior alternatives to perspective-based solutions.<n>We propose Restricted Quadrilateral Representation to define 3D regression targets.<n>RQR3D regresses the smallest horizontal bounding box encapsulating the oriented box, along with the offsets between the corners of these two boxes.
  arXiv  Detail & Related papers  (2025-05-23T10:52:34Z)
• T-3DGS: Removing Transient Objects for 3D Scene Reconstruction [83.05271859398779]
  Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions.<n>We propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting.
  arXiv  Detail & Related papers  (2024-11-29T07:45:24Z)
• Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos [91.44553585470688]
  Multi-person 3D mesh recovery from videos is a critical first step towards automatic perception of group behavior in virtual reality, physical therapy and beyond. We propose the Coordinate transFormer (CoordFormer) that directly models multi-person spatial-temporal relations and simultaneously performs multi-mesh recovery in an end-to-end manner. Experiments on the 3DPW dataset demonstrate that CoordFormer significantly improves the state-of-the-art, outperforming the previously best results by 4.2%, 8.8% and 4.7% according to the MPJPE, PAMPJPE, and PVE metrics, respectively.
  arXiv  Detail & Related papers  (2023-08-20T18:23:07Z)

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.