← Back to all news
arXiv Computer Vision By Bao-Long Tran, Cuong Le, Fredrik Viksten, Per-Erik Forss\'en

GeoBlur: Epipolar Geometry Estimation from a Single Motion-Blurred Image

Read the original on arXiv Computer Vision →

GeoBlur is a framework that estimates the fundamental matrix and relative camera pose from a single motion‑blurred image by exploiting blur artifacts as motion cues. It predicts visual correspondences between two time instances within the exposure window and solves the single‑frame epipolar geometry problem, yielding a fundamental matrix unique up to transposition due to time‑direction ambiguity. The method shows improved performance on synthetic and hybrid benchmarks and remains competitive on real motion‑blur data, also enabling downstream single‑frame motion segmentation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Aug 25

ORBIT++: Benchmarking SfM in the Wild with 360{\deg} Video

arXiv:2608.22039v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging came...

By Sara Sabour, Linyi Jin, Richard Tucker, Amir Hertz, Marcus Brubaker, Saurabh Saxena, Junhwa Hur, Andrea Tagliasacchi, Deqing Sun, David J. Fleet, Richard Szeliski, Noah Snavely
computer-visionbenchmarks
More like this →
arXiv Computer Vision
Sep 15

FFVO: A Feedforward Pose Decoder for Long-Horizon Visual Odometry

arXiv:2609.13733v1 Announce Type: new Abstract: Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate came...

By Meng-Li Shih, Shih-Yang Su, Yuliang Zou, Hao Xiang, Haidong Zhu, Vincent Casser, Brian Curless, Dmitry Kalenichenko, Mingxing Tan, Dragomir Anguelov
agentscomputer-visionroboticsbenchmarks
More like this →
arXiv Computer Vision
Sep 7

MINT: A Unified Model for World-Space Camera and Hand Motion Estimation from Scalable Egocentric Pipeline Supervision

MINT is a foundation model that directly predicts world-space two-hand trajectories from egocentric RGB video, jointly estimating camera motion, hand states, and hand presence in a single spatiotemporal representation. It uses an open-source labeling pipeline, EGOPIPELINE, to generate large-scale pseudo-labels for pretraining, followed by fine-tuning on a small set of high-quality joint annotations. The model outperforms existing multi-stage approaches in accuracy and speed, and generalizes zero‑shot to unseen egocentric datasets.

By Zijie Zhu, Weiren Cai, Yizhou Wang, Zhenjie Yang, Yide Liu, Jiahao Chen, Guanqi He
roboticsfine-tuningbenchmarks
More like this →
arXiv Computer Vision
Sep 18

Monocular Visual Odometry without Calibration or Test-time Optimization

The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.

By Vladimir Yugay, Duy-Kien Nguyen, Theo Gevers, Cees G. M. Snoek, Martin R. Oswald
llmsbenchmarkssafety
More like this →