HiSfM: Disambiguating Structure-from-Motion via Scaffold-Anchored Hierarchical Reconstruction

• URL: http://arxiv.org/abs/2609.04718v1
• Date: Fri, 04 Sep 2026 04:41:24 GMT
• Title: HiSfM: Disambiguating Structure-from-Motion via Scaffold-Anchored Hierarchical Reconstruction
• Abstract summary: We present HiSfM, a hierarchical coarse-to-fine SfM framework that improves robustness and efficiency through scaffold construction.<n> experiments on ambiguity-focused benchmarks and general datasets show that HiSfM prevents ambiguity-induced failures.
• Score: 24.040776393056742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structure-from-Motion (SfM) is a fundamental tool for sparse 3D reconstruction with broad impact in robotics and vision, supporting mapping, localization, and large-scale scene modeling. However, conventional pipelines often fail under hard visual ambiguity caused by repeated or symmetric structures, and incur heavy computational cost due to redundant cameras and constraints. We present HiSfM, a hierarchical coarse-to-fine SfM framework that improves robustness and efficiency through scaffold construction. HiSfM first forms strong local communities using geometrical induced heuristics, then connects communities with a compact yet strong skeleton by packing edge-disjoint spanning trees (EDST) while verifying skeletal edges with a two-view disambiguator. We reconstruct a stable scaffold on this verified skeleton, serving as an anchor to capture the essence of the scene, and subsequently absorb remaining images via efficient registration and triangulation for further refinements. Experiments on ambiguity-focused benchmarks and general datasets show that HiSfM prevents ambiguity-induced failures while substantially reducing runtime compared to previous methods, and improves completeness over aggressive sparsification methods. Code is available at https://github.com/3dv-casia/HiSfM.

Related papers

• MSVS-VAE: Multi-Scale Anchored VecSet for High-Fidelity 3D Reconstruction [39.69838180175452]
  We propose MSVS-VAE, a hierarchical set-based VAE that closes this fidelity gap without sacrificing compactness.<n>Our key idea is to progressively densify anchored VecSet latents via hierarchical point-shuffle upsampling, increasing spatial capacity for fine-grained geometry modeling.<n>Extensive experiments on generative benchmarks, ABO, and in-the-wild benchmarks demonstrate that MSVS-VAE consistently outperforms prior set-based and voxel-based VAEs, delivering approximately 10x faster decoding than prior set-based methods and approximately 10x higher compactness than voxel-based
  arXiv  Detail & Related papers  (2026-07-27T13:45:22Z)
• BA-T: An Iterative Transformer for Two-View Bundle Adjustment [53.16990349592508]
  We propose BA-T, an iterative Transformer that implements BA-style structured updates as a repeatable layer in implicit token space.<n> experiments demonstrate that BA-T progressively improves pose and reconstruction accuracy across iterations.<n>BA-T provides a compact, efficient, and structural alternative to depth-heavy attention, enabling accurate 3D reconstruction within a lightweight architecture.
  arXiv  Detail & Related papers  (2026-06-02T07:51:14Z)
• Global Structure-from-Motion Meets Feedforward Reconstruction [50.69771349993019]
  Structure-from-Motion is a central challenge in computer vision.<n>Recent advances in feedforward 3D reconstruction have made significant strides in overcoming persistent failure cases of classical SfM methods.<n>We propose a new Structure-from-Motion pipeline by combining the respective strengths of classical and feedforward methods.
  arXiv  Detail & Related papers  (2026-05-25T17:58:03Z)
• Mind the Gap: Geometrically Accurate Generative Reconstruction from Disjoint Views [0.8435195043404353]
  We introduce Generative Reconstruction from Disjoint Views as a new paradigm.<n>We propose GLADOS, a general, modular framework that operates through three stages.<n>As an architectureagnostic framework, GLADOS enables seamless integration of future advances in generation, reconstruction, and inpainting.
  arXiv  Detail & Related papers  (2026-05-08T10:24:23Z)
• 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)
• S-VGGT: Structure-Aware Subscene Decomposition for Scalable 3D Foundation Models [15.408916900664783]
  Feed-forward 3D foundation models face a key challenge: the quadratic computational cost introduced by global attention.<n>We introduce textbfS-VGGT, a novel approach that addresses redundancy at the structural frame level.<n>S-VGGT is entirely to token-level acceleration methods, allowing the two to be seamlessly combined for compounded speedups.
  arXiv  Detail & Related papers  (2026-03-18T11:42:55Z)
• Towards Geometry-Aware and Motion-Guided Video Human Mesh Recovery [60.51998732898099]
  We introduce HMRMamba, a new paradigm for 3D Human Mesh Recovery.<n>It pioneers the use of Structured State Space Models for their efficiency and long-range modeling prowess.<n>Our framework is distinguished by two core contributions. First, the Geometry-Aware Lifting Module, featuring a novel dual-scan Mamba architecture.
  arXiv  Detail & Related papers  (2026-01-29T08:05:02Z)
• MetroGS: Efficient and Stable Reconstruction of Geometrically Accurate High-Fidelity Large-Scale Scenes [20.601722393809244]
  We introduce MetroGS, a novel framework for efficient and robust reconstruction in complex urban environments.<n>Our method is built upon a distributed 2D Gaussian Splatting representation as the core foundation.<n>Experiments on large-scale urban datasets demonstrate that MetroGS achieves superior geometric accuracy, rendering quality.
  arXiv  Detail & Related papers  (2025-11-24T14:34:19Z)
• Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction [53.26903617819014]
  Flow-Matching-guided Unfolding network (FMU) is first to integrate flow matching into HSI reconstruction.<n>To further strengthen the learned dynamics, we introduce a mean velocity loss.<n>Experiments on both simulated and real datasets show that FMU significantly outperforms existing approaches in reconstruction quality.
  arXiv  Detail & Related papers  (2025-10-02T11:32:00Z)
• OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting [78.70702961852119]
  OracleGS reconciles generative completeness with regressive fidelity for sparse view Gaussian Splatting.<n>Our approach conditions the powerful generative prior on multi-view geometric evidence, filtering hallucinatory artifacts while preserving plausible completions in under-constrained regions.
  arXiv  Detail & Related papers  (2025-09-27T11:19:32Z)
• Robust Incremental Structure-from-Motion with Hybrid Features [73.55745864762703]
  We introduce an incremental Structure-from-Motion (SfM) system that leverages lines and their structured geometric relations. Our system is consistently more robust and accurate compared to the widely used point-based state of the art in SfM.
  arXiv  Detail & Related papers  (2024-09-29T22:20:32Z)

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.