arXiv AI
Component-Aware Structure-Preserving Style Transfer for Satellite Visual Sim2Real Data Construction
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arXiv Computer Vision
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YOLOv14: Adaptive Real-Time Object Detection for Diverse Imaging Conditions
arXiv:2608. 04720v4 Announce Type: replace Abstract: Real-time object detectors achieve remarkable accuracy under controlled conditions, yet degrade sharply on non-ideal inputs-fisheye distortion, game-rendered content, aerial views, and 360{\deg}panoramas.
arXiv Computer Vision
Sep 10
Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image
arXiv:2606.24138v2 Announce Type: replace Abstract: Generating explicit textured 3D city assets from a single satellite image is important for urban simulation and digital twins. Most prior methods,...
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Benchmarking the Alignment of Data-Quality Metrics, Human Judgment and Land-Cover Segmentation Performance for Earth Observation
arXiv:2606. 25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs.
arXiv AI
Jun 30
FalconTrack: Photorealistic Auto-Labeled Perception and Physics-Aware Vision-Based Aerial Tracking
arXiv:2606. 29783v1 Announce Type: cross Abstract: Vision-based aerial tracking is critical in GPS-denied environments.
arXiv Computer Vision
Aug 28
SpatialCrafter: Single Image World Modeling with Generative 3D Proxies
SpatialCrafter introduces a two‑stage framework for single‑image world modeling that first generates a global 3D proxy using a Point‑anchored Sparse Structure Flow module, then refines appearance with a Generative Deferred Refiner built on a video diffusion model. The method incorporates Parallel Geometry Injection and Proxy‑Aware Corruption training to integrate the proxy without disrupting the pretrained generative manifold, and it is evaluated on a newly constructed dataset of 115K scenes. Experiments demonstrate that SpatialCrafter outperforms existing approaches, reducing long‑term drift and maintaining consistency under rapid camera motion and extreme viewpoints.
arXiv Computer Vision
2d ago
Unlocking Zero-shot Potential of Semi-dense Image Matching via Gaussian Splatting
arXiv:2511.21265v2 Announce Type: replace Abstract: Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) en...