We present PiLoT, a unified framework that tackles UAV-based ego and target geo-localization. Conventional approaches rely on decoupled pipelines that fuse GNSS and Visual-Inertial Odometry (VIO) for ego-pose estimation, and active sensors like laser rangefinders for target localization. However, these methods are susceptible to failure in GNSS-denied environments and incur substantial hardware costs and complexity. PiLoT breaks this paradigm by directly registering live video stream against a geo-referenced 3D map. To achieve robust, accurate, and real-time performance, we introduce three key contributions: 1) a Dual-Thread Engine that decouples map rendering from core localization thread, ensuring both low latency while maintaining drift-free accuracy; 2) a large-scale synthetic dataset with precise geometric annotations (camera pose, depth maps). This dataset enables the training of a lightweight network that generalizes in a zero-shot manner from simulation to real data; and 3) a Joint Neural-Guided Stochastic-Gradient Optimizer (JNGO) that achieves robust convergence even under aggressive motion.Evaluations on a comprehensive set of public and newly collected benchmarks show that PiLoT outperforms state-of-the-art methods while running over 25 FPS on NVIDIA Jetson Orin platform. Our code and dataset are available at: https://github.com/Choyaa/PiLoT.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Monocular UAV videos pose a fundamental challenge for 3D reconstruction: dynamic scene modeling requires accurate camera poses, yet recovering poses from long UAV trajectories often fails in texture-sparse regions and in the presence of moving objects. Existing approaches typically handle either pose-free static reconstruction or dynamic reconstruction with known poses, but jointly solving both from casual aerial footage remains difficult due to motion coupling and severe scale variation. We introduce AeroGS, a scale-aware Gaussian splatting framework that jointly recovers camera trajectories and reconstructs dynamic scenes from pose-free monocular videos. Central to our method are scale-aware spatio-temporal anchors (S2A-Anchors), which enable a unified optimization via three key decoupling mechanisms: (i) separating ego-motion from object motion, (ii) isolating static geometry from temporal deformation, and (iii) adapting to scale variation between distant terrain and nearby objects. This design effectively stabilizes optimization under large motion and scale imbalance. Extensive experiments on UAV and driving benchmarks show that AeroGS achieves state-of-the-art rendering quality (PSNR/LPIPS), precise trajectory recovery (ATE/RPE), and faithful motion reconstruction, consistently surpassing recent pose-free baselines.
尚无 AI 解读，展示原文摘要
cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表