Event-Based Motion Estimation via Oriented Distance Fields
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The paper introduces a new framework for estimating full-degree-of-freedom egomotion directly from asynchronous optical flow captured by event cameras. By decoupling the differential epipolar constraint into angular and linear components and applying a first-order approximation, the authors derive a polynomial formulation that yields the first algebraic minimal 5‑point solver for this problem. An accelerated solver that truncates high‑order angular velocity terms is also proposed, enabling real‑time performance in high‑speed scenarios, and extensive tests show superior accuracy and robustness compared to traditional synchronous methods.
arXiv:2608. 15024v1 Announce Type: cross Abstract: Current Vision-based SLAM systems fail catastrophically when motion blur corrupts the visual input, as they attempt the ill-posed inverse problem of recovering sharp content from degraded observations.
arXiv:2608.22398v1 Announce Type: cross Abstract: Reliably tracking moving deformable linear objects (DLOs) while simultaneously ensuring robustness, accuracy, and temporally consistent state estimat...
The paper presents a lightweight, training‑free framework for real‑time unsupervised object discovery from asynchronous event camera streams. It introduces a linear‑time Spatio‑Temporal Probabilistic Event Filter (SPEF) that adaptively distinguishes salient motion from noise, and an Event Morton Code Clustering (EMCC) module that efficiently groups events without costly distance calculations. Experiments on E‑MLB, FRED, and eTraM datasets show SPEF outperforms classical filters and competes with learning‑based methods, while EMCC achieves the highest accuracy and fastest execution among density‑based clustering baselines.
SSMB is a self‑supervised keypoint detector designed for motion‑blurred images that does not rely on handcrafted detectors or external pseudo‑labels. It introduces a Local Discriminability Enhancement module to recover fine‑grained local detail after global feature mixing, and is trained in two stages: geometric pretraining on synthetic shapes and blur‑aware training on real sharp‑blur pairs using a multi‑component self‑supervised objective. Extensive experiments show that SSMB outperforms both supervised and self‑supervised baselines on keypoint detection, image matching, relative pose estimation, and visual localization under motion blur, achieving state‑of‑the‑art performance.
arXiv:2606. 14094v1 Announce Type: cross Abstract: Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information.