Extreme Rotation Estimation in the Wild

• URL: http://arxiv.org/abs/2411.07096v2
• Date: Tue, 12 Nov 2024 12:45:03 GMT
• Title: Extreme Rotation Estimation in the Wild
• Authors: Hana Bezalel, Dotan Ankri, Ruojin Cai, Hadar Averbuch-Elor,
• Abstract summary: We present a technique for estimating the relative 3D orientation between a pair of Internet images captured in an extreme setting. We contribute the ExtremeLandmarkPairs dataset, assembled from scene-level Internet photo collections.
• Score: 11.5425189881311
• License:
• Abstract: We present a technique and benchmark dataset for estimating the relative 3D orientation between a pair of Internet images captured in an extreme setting, where the images have limited or non-overlapping field of views. Prior work targeting extreme rotation estimation assume constrained 3D environments and emulate perspective images by cropping regions from panoramic views. However, real images captured in the wild are highly diverse, exhibiting variation in both appearance and camera intrinsics. In this work, we propose a Transformer-based method for estimating relative rotations in extreme real-world settings, and contribute the ExtremeLandmarkPairs dataset, assembled from scene-level Internet photo collections. Our evaluation demonstrates that our approach succeeds in estimating the relative rotations in a wide variety of extreme-view Internet image pairs, outperforming various baselines, including dedicated rotation estimation techniques and contemporary 3D reconstruction methods.

Related papers

• Cameras as Rays: Pose Estimation via Ray Diffusion [54.098613859015856]
  Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views. We propose a distributed representation of camera pose that treats a camera as a bundle of rays. Our proposed methods, both regression- and diffusion-based, demonstrate state-of-the-art performance on camera pose estimation on CO3D.
  arXiv  Detail & Related papers  (2024-02-22T18:59:56Z)
• Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation [45.11207941777178]
  This paper introduces a novel approach to the fine alignment of images in a burst captured by a handheld camera. The proposed algorithm establishes dense correspondences by optimizing both the camera motion and surface depth and orientation at every pixel.
  arXiv  Detail & Related papers  (2023-12-08T17:22:04Z)
• Calibrating Panoramic Depth Estimation for Practical Localization and Mapping [20.621442016969976]
  The absolute depth values of surrounding environments provide crucial cues for various assistive technologies, such as localization, navigation, and 3D structure estimation. We propose that accurate depth estimated from panoramic images can serve as a powerful and light-weight input for a wide range of downstream tasks requiring 3D information.
  arXiv  Detail & Related papers  (2023-08-27T04:50:05Z)
• FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models [67.96827539201071]
  We propose a novel test-time optimization approach for 3D scene reconstruction. Our method achieves state-of-the-art cross-dataset reconstruction on five zero-shot testing datasets.
  arXiv  Detail & Related papers  (2023-08-10T17:55:02Z)
• RelPose++: Recovering 6D Poses from Sparse-view Observations [66.6922660401558]
  We address the task of estimating 6D camera poses from sparse-view image sets (2-8 images) We build on the recent RelPose framework which learns a network that infers distributions over relative rotations over image pairs. Our final system results in large improvements in 6D pose prediction over prior art on both seen and unseen object categories.
  arXiv  Detail & Related papers  (2023-05-08T17:59:58Z)
• Extreme Rotation Estimation using Dense Correlation Volumes [73.35119461422153]
  We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting. We observe that, even when images do not overlap, there may be rich hidden cues as to their geometric relationship. We propose a network design that can automatically learn such implicit cues by comparing all pairs of points between the two input images.
  arXiv  Detail & Related papers  (2021-04-28T02:00:04Z)
• 3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning [11.599633757222406]
  Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods. In this work, we propose a compact network for absolute camera pose regression. Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents.
  arXiv  Detail & Related papers  (2020-05-13T04:15:14Z)
• 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)
• Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images [59.906948203578544]
  We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object. We first estimate per-view depth maps using a deep multi-view stereo network. These depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture.
  arXiv  Detail & Related papers  (2020-03-27T21:28:54Z)

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.