AirZoo is a new large-scale synthetic dataset for aerial 3D vision that improves state-of-the-art models on image retrieval, cross-view matching, and 3D reconstruction when used for fine-tuning.
arXiv preprint arXiv:2505.12549 (2025)
9 Pith papers cite this work. Polarity classification is still indexing.
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CAL2M achieves calibration-free kilometer-level SLAM by using an assistant eye for scale, epipolar-guided intrinsic correction, and anchor propagation for nonlinear sub-map alignment.
Ray-aware pointer memory with adaptive retain-or-replace updates enhances stability and accuracy in streaming 3D reconstruction.
RADIO-ViPE performs online open-vocabulary semantic SLAM directly from monocular RGB video in dynamic environments by tightly coupling vision-language embeddings from foundation models with geometric factor-graph optimization using adaptive robust kernels.
Scal3R achieves better accuracy and consistency in large-scale 3D scene reconstruction by maintaining a compressed global context through test-time adaptation of lightweight neural networks on long video sequences.
ZeD-MAP integrates incremental cluster-based bundle adjustment with zero-shot diffusion depth estimation to deliver metrically consistent real-time depth maps from high-resolution UAV imagery.
DA3 recovers consistent visual geometry from arbitrary views via a vanilla DINO transformer and depth-ray target, setting new SOTA on a visual geometry benchmark while outperforming DA2 on monocular depth.
MonoEM-GS stabilizes view-dependent geometry from foundation models inside a global Gaussian Splatting representation via EM and adds multi-modal features for in-place open-set segmentation.
VGGT-SLAM++ improves on prior transformer SLAM by adding dense DEM submap graphs and high-cadence local optimization, achieving SOTA accuracy with reduced drift and bounded memory on benchmarks.
citing papers explorer
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AirZoo: A Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision
AirZoo is a new large-scale synthetic dataset for aerial 3D vision that improves state-of-the-art models on image retrieval, cross-view matching, and 3D reconstruction when used for fine-tuning.
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Keep It CALM: Toward Calibration-Free Kilometer-Level SLAM with Visual Geometry Foundation Models via an Assistant Eye
CAL2M achieves calibration-free kilometer-level SLAM by using an assistant eye for scale, epipolar-guided intrinsic correction, and anchor propagation for nonlinear sub-map alignment.
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Ray-Aware Pointer Memory with Adaptive Updates for Streaming 3D Reconstruction
Ray-aware pointer memory with adaptive retain-or-replace updates enhances stability and accuracy in streaming 3D reconstruction.
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RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments
RADIO-ViPE performs online open-vocabulary semantic SLAM directly from monocular RGB video in dynamic environments by tightly coupling vision-language embeddings from foundation models with geometric factor-graph optimization using adaptive robust kernels.
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Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction
Scal3R achieves better accuracy and consistency in large-scale 3D scene reconstruction by maintaining a compressed global context through test-time adaptation of lightweight neural networks on long video sequences.
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ZeD-MAP: Bundle Adjustment Guided Zero-Shot Depth Maps for Real-Time Aerial Imaging
ZeD-MAP integrates incremental cluster-based bundle adjustment with zero-shot diffusion depth estimation to deliver metrically consistent real-time depth maps from high-resolution UAV imagery.
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Depth Anything 3: Recovering the Visual Space from Any Views
DA3 recovers consistent visual geometry from arbitrary views via a vanilla DINO transformer and depth-ray target, setting new SOTA on a visual geometry benchmark while outperforming DA2 on monocular depth.
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MonoEM-GS: Monocular Expectation-Maximization Gaussian Splatting SLAM
MonoEM-GS stabilizes view-dependent geometry from foundation models inside a global Gaussian Splatting representation via EM and adds multi-modal features for in-place open-set segmentation.
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VGGT-SLAM++
VGGT-SLAM++ improves on prior transformer SLAM by adding dense DEM submap graphs and high-cadence local optimization, achieving SOTA accuracy with reduced drift and bounded memory on benchmarks.