DAPEVO is a learned visual odometry system that independently estimates image and event correspondences at shared patch locations and fuses their correlation evidence before motion refinement. It maintains image and event descriptors for each tracked patch, using a learned scalar gate to combine modality-specific correlation embeddings for each patch–frame edge, followed by a shared recurrent refinement and bundle‑adjustment update. The method supports event‑only observations and modality‑aware keyframe culling, achieving low trajectory error even when RGB frames are sparse or degraded, outperforming DPVO, RAMP‑VO, and event‑only DEVO on UZH‑FPV and TartanEvent datasets.
By Luca Gandolfi, Simone Nascivera, Roberto Pellerito, Rong Zou, Chiara Plizzari, Davide Scaramuzza
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.
By Shuo Pan, Banglei Guan, Bin Li, Zhenbao Yu, Zibin Liu, Zi Wang, Yang Shang, Qifeng Yu
arXiv:2610.33356v1 Announce Type: new
Abstract: Event cameras provide microsecond temporal resolution, high dynamic range, and sparse asynchronous measurements, making them compelling for odometry un...
By Matteo Cascone, David Jensen, Alessandro Pirini, Angelo Stefanini, Pietro Ven\`e, Marta Tintor\'e Gazulla, Javier Hidalgo Carri\`o
arXiv:2609.22500v1 Announce Type: new
Abstract: Autonomous navigation requires precise and efficient semantic segmentation, yet existing frame-based approaches remain limited by motion blur, glare, l...
By Dalia Hareb, Jean Martinet, Benoit Miramond, Elisabetta Chicca
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.
By Zhiqiang Hu, Shouren Huang, Masatoshi Ishikawa
arXiv:2607.08098v2 Announce Type: replace
Abstract: Event cameras are increasingly adopted in embodied perception for their microsecond temporal resolution, high dynamic range, and resilience to moti...
By Linli Shi, Ruijun Zhang, Ziyun Wang
arXiv:2608.22398v1 Announce Type: cross
Abstract: Reliably tracking moving deformable linear objects (DLOs) while simultaneously ensuring robustness, accuracy, and temporally consistent state estimat...
By Annalena Hartmann, Priyamvada Ajithkumar, Patrick Br\"undl, J\"org Franke
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.
By Pratham G. Shenwai, Hemant Kumar Singh, Sridhar Ravi
arXiv:2609.23408v1 Announce Type: new
Abstract: In frame interpolation tasks, motion ambiguity in the training set causes models to generate blurry intermediate frames. Moreover, the assumption of un...
By Shuhao Han, Chenyang Wu, Chun-Le Guo, Zheng-Peng Duan, Zhen Li, Ming-Ming Cheng, Chongyi Li
arXiv:2609.25803v1 Announce Type: new
Abstract: High-rate dense perception in dynamic environments is limited by the low update rate of RGB cameras, as rapid scene changes can occur between frames. E...
By Tao Wan, Xiaoshan Wu, Yifei Yu, Bo Wang, Xiaoyang Lyu, Muxin Liu, Aoxuan Pan, Zhongrui Wang, Xiaojuan Qi
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.
By Zhenjun Zhao, Fabio Bellavia, Wenting Wang, Fan Zhu, Jiajun Wu, Suryansh Kumar, Mingqiang Wei, Haoang Li, Javier Civera
The paper introduces SCARF, a real‑time velocity‑invariant representation for event cameras that preserves raw events while handling fast motion, stationary scenes, and independently moving objects. Unlike previous methods that convert events into image‑like forms and lose temporal detail, SCARF maintains temporal information and achieves state‑of‑the‑art performance in computational efficiency and representation quality.
By Mikihiro Ikura, Luna Gava, Jiahang Wu, Chiara Bartolozzi, Arren Glover