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arXiv Computer Vision

Understanding Temporal Semantic Stability in Open-Vocabulary UAV Perception through Metric 3D Fusion

The paper studies how open‑vocabulary segmentation models perform on UAV footage, focusing on temporal consistency of predictions. By linking frame‑wise outputs to persistent 3D voxels via metric fusion, the authors propose a voxel‑level evaluation that measures final agreement, Semantic Belief Drift, Observation Persistence, and uncertainty. Experiments on UAVid‑3D show that high overall agreement can mask instability when observations are sparse, and that persistence‑stratified analysis reveals greater disagreement for recurrent voxels while belief drift reduces with more evidence.

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VoxelFix: Post-Hoc Semantic Correction of Completed 3D Voxel Maps

VoxelFix is a graph‑based post‑hoc semantic correction method that refines voxel labels in completed 3D voxel maps while preserving their geometry and occupancy. It learns to correct errors by exploiting local geometry and neighboring semantic information, using training pairs generated by corrupting annotated maps with class confusions from upstream perception pipelines. Experiments on OccuFly maps show consistent improvements of 4.23–5.00 percentage points in mIoU, especially for tree, roof, and wall classes, and the method generalizes to out‑of‑distribution aerial scenes.

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arXiv Computer Vision
5d ago

Beyond Appearance: Can Multimodal Large Language Models Exploit Vertical Structure for Remote Sensing Natural Scene Understanding?

The paper introduces VertiCue-Bench, a diagnostic benchmark designed to test whether multimodal large language models (MLLMs) can perceive, ground, and utilize vertical structure information in remote-sensing natural scenes. It presents a three-stage framework—Perception, Grounding, Utilization—and a Representation Intervention Spectrum across various modalities to evaluate ten state-of-the-art models. The study finds a significant Vertical Structure Utilization Gap: while models show some geometric perception, they struggle to accurately link vertical evidence to spatial entities and incorporate it into semantic decisions.

By Jing Huang, Duanchu Wang, Junjie Yang, Zihang Cheng, Cheng Li, Lin Cui, Zhouyi Wu, Di Wang
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Hugging Face Trending Papers
Jul 27

InterOCF: Spatio-Temporal 2D-3D Interaction for Camera-Only 4D Occupancy Forecasting

Camera-only 4D occupancy forecasting enables autonomous vehicles to predict future 3D semantic scenes solely from historical multi-view images, which is critical for driving safety. Even though current methods have achieved good performance, the strong spatial-temporal modeling between the input multi-view frames is still underexplored, which limits the performance of those methods in future 4D forecasting.

agentscomputer-visionsafety
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