Accurate six-degree-of-freedom (6-DOF) motion estimation is essential for robotic manipulation, autonomous systems, and structural displacement monitoring. Conventional 3D-2D methods estimate absolute camera poses independently at each time and recover platform motion through camera-to-platform extrinsics, making them sensitive to extrinsic calibration errors, especially for micromotion.
The paper introduces a minimalist visual-inertial odometry system that uses only four downward-facing photodiodes with optical Gabor masks and an IMU to estimate motion for differential-drive robots. By jointly optimizing mask parameters and a Temporal Convolutional Network in a physically-grounded simulator, the model decodes speed from the photodiode signals and combines it with IMU angular speed to produce a continuous planar trajectory. Experiments on a prototype robot across indoor and outdoor terrains show that the system closely follows reference trajectories without real-world fine-tuning.
By Francesco Pasti, Jeremy Klotz, Nicola Bellotto, Shree K. Nayar
arXiv:2608.24223v1 Announce Type: new
Abstract: Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion. Existing methods typically rely o...
By Lei Sun, Yuqin Ma, Weilun Li, Haoran Liang, Runyi Yang, Kaiwei Wang, Danda Pani Paudel, Luc Van Gool
arXiv:2609.01276v1 Announce Type: new
Abstract: Complete 3D perception from egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Exis...
By Kai Guan, Minchao Jiang, Ruichen WangLi, Wentao Zhu, Lei Zhang
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
arXiv:2608.22861v1 Announce Type: new
Abstract: State Space Models (SSMs) have surfaced as a promising architecture in Video Frame Interpolation (VFI), as they can capture long-range dependencies wit...
By Jaehyun Park, Nam Ik Cho
ASTRA (Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignment) tackles the challenge of reconstructing dynamic 3D scenes from temporally asynchronous multi‑camera data. By using 2D motion trajectories as texture‑robust supervision, it jointly optimizes temporal offsets and 3D representations, aligning projected 3D point motion with observed 2D paths while masking unreliable constraints. Experiments on Gaussian Splatting backbones show that ASTRA retains high‑frequency spatial detail, improves PSNR by ~1.4 dB, reduces temporal‑offset MAE by 54 %, and nearly quadruples synchronization success even with up to 25‑frame offsets.
By Junyu Zhu, Hao Zhu, Xinzhuo Zhang, Xu Zhang, Hongdong Li, Zhan Ma, Xun Cao
Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment. A holistic and efficient multimodal model capable of reconstructing this 4D representation is therefore highly desirable.
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
arXiv:2607. 17790v1 Announce Type: cross Abstract: Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment.
By Xiaozhong Lyu, Gen Li, Zhiyin Qian, Xucong Zhang, Marc Pollefeys, Siyu Tang
DefVINS is a visual‑inertial odometry pipeline tailored for deformable scenes, breaking the rigidity assumption of traditional VIO. It decomposes the odometry state into a rigid, IMU‑anchored component and a non‑rigid scene warp using an embedded deformation graph. The authors also introduce VIMandala, the first real‑world benchmark with ground‑truth camera poses for deformable VIO, and extend the synthetic Drunkard’s benchmark with inertial data, demonstrating that DefVINS outperforms both rigid and non‑rigid baselines.
By Samuel Cerezo, Javier Civera
arXiv:2608.29208v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models, built upon Vision-Language Models (VLMs), have significantly enhanced robotic capabilities by leveraging interne...
By Sunghwan Han, Youngtae Han, Youngmin Yi