arXiv Computer Vision
RIGOR: Rig-Informed Geometry for Omnidirectional Reconstruction
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arXiv Computer Vision
Aug 31
GeoFF3D: Coordinate-Anchored Feed-Forward Reconstruction for Large-Scale UAV Mapping
GeoFF3D is a new feed‑forward 3D reconstruction method designed for large‑scale UAV mapping. It uses a coordinate‑anchored model that predicts camera poses and dense point maps directly in a gravity‑aligned Z‑up metric frame, while a spatial large‑scale reconstruction framework (SLRF) partitions images into overlapping chunks, propagates shared‑view priors, and aggregates local reconstructions hierarchically. Across nine aerial mapping blocks, GeoFF3D achieves the best average reconstruction quality, improving F@5 from 0.829 to 0.877, and can reconstruct 2,000 images in about five minutes.
arXiv Computer Vision
Sep 2
On-the-Fly3R: Towards Robust Online 3D Reconstruction with Feed-Forward 3R Models for Large-Scale UAV Scenarios
arXiv:2609.00923v1 Announce Type: new Abstract: While feed-forward 3D reconstruction (3R) offers efficient end-to-end modeling, its application in large-scale UAV mapping is hindered by the prohibiti...
arXiv Computer Vision
Aug 25
ORBIT++: Benchmarking SfM in the Wild with 360{\deg} Video
arXiv:2608.22039v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging came...
arXiv Computer Vision
3d ago
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
3d ago
AlayaVista: Streaming World Modeling from Panoramic States to Perspective Video
arXiv:2609.14462v1 Announce Type: new Abstract: Interactive video world models must maintain broad scene context under camera motion while producing high-fidelity observations with low latency. Exist...
arXiv AI
Jul 2
Pano2World: End-to-End 3D Generation via Unified Multi-View Sequences
arXiv:2607. 00832v1 Announce Type: cross Abstract: A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration.