arXiv Computer Vision
MIRAGE: Metric Inference and Reliability-Aware Gaussian Estimation from Feed-Forward Vision
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arXiv Computer Vision
Sep 3
TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views
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 Computer Vision
Sep 22
SURE-Map: Self-Correcting Streaming Geometric Foundation Models
arXiv:2609.15795v2 Announce Type: replace Abstract: Streaming geometric foundation models are emerging as a compelling alternative to SLAM systems. Yet this streaming nature introduces a fundamental...
arXiv Computer Vision
Sep 15
SURE-Map: Self-Correcting Streaming Geometric Foundation Model
arXiv:2609.15795v1 Announce Type: new Abstract: Streaming geometric foundation models are emerging as a compelling alternative to SLAM systems. Yet this streaming nature introduces a fundamental issu...
arXiv Computer Vision
Sep 18
Monocular Visual Odometry without Calibration or Test-time Optimization
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.
arXiv Computer Vision
Sep 15
Anchor3R: Streaming 3D Reconstruction with Transient Anchors for Long-Horizon Visual Mapping
Anchor3R is a streaming 3D reconstruction framework that predicts window-relative poses and local geometry in the current‑frame coordinate system, forming a dense relative‑pose graph for online pose updates and loop‑aware motion averaging. It improves long‑horizon pose accuracy and dense reconstruction quality on indoor, outdoor, driving, and RGB‑D benchmarks, and generalizes from 48‑frame training sequences to streams exceeding 10,000 frames while keeping GPU memory bounded. The method addresses issues of train‑test mismatch, early‑anchor bias, and accumulated drift found in previous streaming models.
arXiv Computer Vision
Sep 21
Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering
arXiv:2504.15776v2 Announce Type: replace Abstract: Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccur...