arXiv Computer Vision
FREE-LIVO: Physically Decoupled Yet Tightly Coupled LiDAR-Inertial-Visual Odometry
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arXiv Computer Vision
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Monocular Visual Odometry without Calibration or Test-time Optimization
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arXiv AI
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Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching
arXiv:2506. 22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception.
arXiv Computer Vision
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DefVINS: Visual-Inertial Odometry for Deformable Scenes
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.
arXiv Computer Vision
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M3GD: Multi-Modal Multi-View Geometric Diffusion for Camera--LiDAR Novel View Synthesis
M3GD introduces a multimodal representation that fuses pre‑trained 2D image and 3D LiDAR foundation models for robotic novel view synthesis, avoiding the need for a separate cross‑modal translator. By projecting LiDAR onto the image latent grid and injecting the resulting geometry‑aware packets via a lightweight residual adapter, the method enhances both RGB and depth synthesis on the GrandTour dataset compared to an image‑only baseline. Ablation studies confirm that pixel‑aligned LiDAR content drives the performance gains, and real‑world deployment on a ground robot demonstrates a tunable quality–cost trade‑off.
arXiv Computer Vision
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Temporal-Aware Fusion for Robust Outdoor LiDAR Localization
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