Con-DSO introduces a consistency-aware RGB‑D direct sparse odometry framework that learns pixel‑level photometric and geometric uncertainty from adjacent RGB‑D frame pairs. The network predicts uncertainties that are converted into pairwise quality scores, guiding support‑pixel selection and forming a host‑side quality prior for keyframe tracking. Experiments on five public benchmarks show that this approach reduces absolute trajectory error by over 20% on ICL‑NUIM and by 50–80% on other datasets, improving robustness in challenging environments.
By Haolan Zhang, Thanh Nguyen Canh, Chenghao Li, Ziyan Gao, Xiongwen Jiang, Nak Young Chong
The paper introduces CELL, a confidence-weighted event-camera localization method that learns per-correspondence confidence end-to-end using a differentiable probabilistic PnP. It reweights flow supervision, drives probabilistic correspondence selection at test time, and refines edge matching, while also proposing a partial-completion depth representation. Experiments on M3ED and DSEC show significant reductions in median translation and rotation errors compared to the LEAR baseline.
By Panagiotis Kiousis, Kuangyi Chen, Jun Zhang, Friedrich Fraundorfer
FlowMoDE introduces a coarse‑to‑fine flow‑matching framework for monocular depth estimation that balances efficiency and detail reconstruction. The method first decodes coarse depth conditioned on diffused features from a pre‑trained semantic encoder, then refines fine structural patterns with expanded decoding layers. Trained only on a synthetic indoor dataset, FlowMoDE achieves a 14.1% lower relative error and a 9.9% lower Chamfer distance compared to state‑of‑the‑art methods across five real‑world indoor and outdoor datasets, and demonstrates strong generalization to realistic robotic scenes.
By Liangjing Shao, Wanhao Liu, Jinsong Lin, Zhiwei Fang, Hongliang Ren
MDE-VIO integrates learned depth priors into the VINS-Mono optimization backend to improve visual‑inertial odometry in low‑texture environments. The framework enforces affine‑invariant depth consistency and pairwise ordinal constraints while filtering unstable artifacts with variance‑based gating, keeping computation within edge‑device limits. Experiments on TartanGround and M3ED datasets show the method prevents divergence and reduces Absolute Trajectory Error by up to 28.3%.
By Arda Alniak, Sinan Kalkan, Mustafa Mert Ankarali, Afsar Saranli, Abdullah Aydin Alatan
RoPE-Flow is an end‑to‑end framework that estimates camera‑to‑robot pose from a single monocular image using flow matching. It extracts keypoint‑centered features from predicted 2D heatmaps and employs a graph‑guided condition generator to encode keypoint correlations for flow matching. The method introduces Bone Length Loss and Ray‑Depth Loss to enforce topological consistency and scale stability, and demonstrates superior performance over state‑of‑the‑art approaches on multiple robot types and a new cross‑scene benchmark.
By Liangjing Shao, Wanhao Liu, Zhiwei Fang, Rulin Zhou, Quanlu Zhang, Changjing Liu, Beilei Cui, Yiming Huang, Hongliang Ren
MetricTrail is a streaming system that accurately estimates camera motion and performs dense 3D reconstruction from long monocular videos. It leverages single‑image metric depth to improve trajectory accuracy without retraining the correspondence network or enlarging the optimization window, and uses learned adapters to enhance feature matching with frozen DINOv3 representations. The modular design allows independent upgrades of the depth provider, supports supplied camera intrinsics and calibrated stereo data, and achieves the lowest average trajectory error on KITTI, Oxford Spires, and VBR under RGB‑only protocols while delivering strong dense reconstructions on indoor, outdoor, and in‑the‑wild benchmarks.
By Jingnan Gao, Jiahui Huang, Sven Elflein, Jianhao Zheng, Bingxin Ke, Xiaokang Yang, Yichao Yan, Laura Leal-Taix\'e, Qunjie Zhou, Jiawei Ren, Shengyu Huang