arXiv:2611.07463v1 Announce Type: cross Abstract: LiDAR-Inertial-Visual Odometry (LIVO) integrates complementary LiDAR, camera, and IMU measurements to achieve robust state estimation. However, most...
arXiv:2608.22965v1 Announce Type: new Abstract: Accurate extrinsic calibration between event-based and frame-based cameras remains a practical bottleneck for heterogeneous stereo systems. Existing ap...
CalibSwitch is an observation‑driven framework for Camera–LiDAR perception that separates absolute extrinsic recovery from selective state maintenance. It introduces a cross‑modal absolute extrinsic generator (CAEG) that recovers full SE(3) candidates from image–point observations without needing an initial transform, and a selective recurrent calibration policy (SRCP) that decides when to update the working geometry based on observation quality and temporal history. On the nuScenes dataset, CAEG achieves mean translation and rotation errors of 0.248 m and 5.884°, while SRCP reduces composite error to 1.46 with only 9.70 % candidate commitments, improving frozen‑detector mAP and NDS by 1.43 and 0.92 points respectively.
arXiv:2610.35554v1 Announce Type: new Abstract: Structure-from-motion (SfM) from the dual-fisheye video of a consumer 360-degree camera recovers geometry only up to a similarity transform; in the pip...
Structure-from-motion (SfM) from the dual-fisheye video of a consumer 360-degree camera recovers geometry only up to a similarity transform; in the pipeline considered here, metric scale is obtained f...
TAPVid-MV is a new benchmark for tracking any point in 3D across multiple synchronized camera views. It comprises 284 sequences, 1,142 calibrated camera streams, and 109,769 point tracks, covering indoor and outdoor domains and derived from various modalities such as depth, LiDAR, SLAM, and simulation. The dataset is visually verified, and evaluation shows that current multi‑view trackers do not consistently outperform monocular trackers, highlighting geometry recovery as a key bottleneck.
arXiv:2606. 25619v2 Announce Type: replace Abstract: In this paper, we present ScaleHP, a unified framework that explicitly represents per-instance metric scale to resolve the coupled errors in calibrated camera-space hand pose estimation.
The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.
The paper introduces TempLoc, a Temporal‑aware Localization framework that improves outdoor LiDAR relocalization by leveraging spatio‑temporal consistency across scans. It first predicts point‑wise global coordinates with uncertainties, then estimates inter‑frame correspondences using an attention‑based Prior Coordinate Generation module, and finally fuses these predictions in an uncertainty‑guided manner to produce a more accurate global 6‑DoF pose. Experiments on the NCLT and Oxford RobotCar datasets show that TempLoc significantly outperforms existing state‑of‑the‑art methods.
arXiv:2607. 02561v1 Announce Type: cross Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse.
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.
The paper introduces an on-the-fly homography calibration system for multi-camera tracking that starts from coarse manual homographies and refines them using a centroid-based projection optimization (PO) on live detection metadata. PO continuously aligns ground-plane geometry without adding computational latency, enabling the system to adapt automatically to camera movements or environmental changes. The refined geometry feeds a bird's-eye-view tracker that fuses detections and unifies trajectories across zones while maintaining privacy safety and zero overhead.