DP-SfM: Dual-Pixel Structure-from-Motion without Scale Ambiguity

• URL: http://arxiv.org/abs/2605.01852v1
• Date: Sun, 03 May 2026 12:45:17 GMT
• Title: DP-SfM: Dual-Pixel Structure-from-Motion without Scale Ambiguity
• Abstract summary: Multi-view 3D reconstruction suffers from an unknown scale ambiguity unless a reference object of known size is present in the scene.<n>We show that multi-view images captured using a dual-pixel (DP) sensor can automatically resolve the scale ambiguity.
• Score: 25.991973883422677
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-view 3D reconstruction, namely, structure-from-motion followed by multi-view stereo, is a fundamental component of 3D computer vision. In general, multi-view 3D reconstruction suffers from an unknown scale ambiguity unless a reference object of known size is present in the scene. In this article, we show that multi-view images captured using a dual-pixel (DP) sensor can automatically resolve the scale ambiguity, without requiring a reference object or prior calibration. Specifically, the defocus blur observed in DP images provides sufficient information to determine the absolute scale when paired with depth maps (up to scale) recovered from multi-view 3D reconstruction. Based on this observation, we develop a simple yet effective linear method to estimate the absolute scale, followed by the intensity-based optimization stage that aligns the left and right DP images by shifting them back toward each other using cross-view blur kernels. Experiments demonstrate the effectiveness of the proposed approach across diverse scenes captured with different cameras and lenses. Code and data are available at https://github.com/lilika-makabe/dp-sfm-tpami.git

Related papers

• OmniPoint: Universal Monocular Metric Pointcloud from Any Camera [79.24417597712802]
  We present OmniPoint, a unified framework designed to generalize metric reconstruction across diverse imaging sensors.<n>To overcome projection rigidity, OmniPoint abandons conventional planar depth regression.<n>It instead adopts a decoupled ray and distance representation alongside a decoupled training objective.
  arXiv  Detail & Related papers  (2026-09-08T19:45:58Z)
• MapAnything: Universal Feed-Forward Metric 3D Reconstruction [63.79151976126576]
  MapAnything ingests one or more images along with optional geometric inputs such as camera intrinsics, poses, depth, or partial reconstructions.<n>It then directly regresses the metric 3D scene geometry and cameras.<n>MapAnything addresses a broad range of 3D vision tasks in a single feed-forward pass.
  arXiv  Detail & Related papers  (2025-09-16T18:00:14Z)
• MUSt3R: Multi-view Network for Stereo 3D Reconstruction [11.61182864709518]
  We propose an extension of DUSt3R from pairs to multiple views, that addresses all aforementioned concerns.<n>We entail the model with a multi-layer memory mechanism which allows to reduce the computational complexity.<n>The framework is designed to perform 3D reconstruction both offline and online, and hence can be seamlessly applied to SfM and visual SLAM scenarios.
  arXiv  Detail & Related papers  (2025-03-03T15:36:07Z)
• DUSt3R: Geometric 3D Vision Made Easy [8.471330244002564]
  We introduce DUSt3R, a novel paradigm for Dense and Unconstrained Stereo 3D Reconstruction of arbitrary image collections.<n>We show that this formulation smoothly unifies the monocular and binocular reconstruction cases.<n>Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, relative and absolute camera.
  arXiv  Detail & Related papers  (2023-12-21T18:52:14Z)
• Visual Geometry Grounded Deep Structure From Motion [20.203320509695306]
  We propose a new deep pipeline VGGSfM, where each component is fully differentiable and can be trained in an end-to-end manner. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.
  arXiv  Detail & Related papers  (2023-12-07T18:59:52Z)
• Monocular 3D Object Detection with Depth from Motion [74.29588921594853]
  We take advantage of camera ego-motion for accurate object depth estimation and detection. Our framework, named Depth from Motion (DfM), then uses the established geometry to lift 2D image features to the 3D space and detects 3D objects thereon. Our framework outperforms state-of-the-art methods by a large margin on the KITTI benchmark.
  arXiv  Detail & Related papers  (2022-07-26T15:48:46Z)
• Facial Depth and Normal Estimation using Single Dual-Pixel Camera [81.02680586859105]
  We introduce a DP-oriented Depth/Normal network that reconstructs the 3D facial geometry. It contains the corresponding ground-truth 3D models including depth map and surface normal in metric scale. It achieves state-of-the-art performances over recent DP-based depth/normal estimation methods.
  arXiv  Detail & Related papers  (2021-11-25T05:59:27Z)
• VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild [98.69191256693703]
  We present VoxelTrack for multi-person 3D pose estimation and tracking from a few cameras which are separated by wide baselines. It employs a multi-branch network to jointly estimate 3D poses and re-identification (Re-ID) features for all people in the environment. It outperforms the state-of-the-art methods by a large margin on three public datasets including Shelf, Campus and CMU Panoptic.
  arXiv  Detail & Related papers  (2021-08-05T08:35:44Z)
• Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo [71.59494156155309]
  Existing approaches for multi-view 3D pose estimation explicitly establish cross-view correspondences to group 2D pose detections from multiple camera views. We present our multi-view 3D pose estimation approach based on plane sweep stereo to jointly address the cross-view fusion and 3D pose reconstruction in a single shot.
  arXiv  Detail & Related papers  (2021-04-06T03:49:35Z)
• Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation [57.11299763566534]
  We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
  arXiv  Detail & Related papers  (2020-04-05T12:52:29Z)

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.