VEGA improves spatial reasoning in VLA models for robotics by aligning visual encoder features with 3D-supervised DINOv2 representations via a temporary projector and cosine similarity loss.
Depth anything v2.Advances in Neural Information Processing Systems, 37:21875– 21911
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
FocusDepth is a prompt-conditioned framework that fuses SAM3 features into Depth Anything models via Multi-Scale Spatial-Aligned Fusion to improve target-region depth accuracy on the new FDE-Bench.
SpatialForge synthesizes 10 million spatial QA pairs from in-the-wild 2D images to train VLMs for better depth ordering, layout, and viewpoint-dependent reasoning.
citing papers explorer
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VEGA: Visual Encoder Grounding Alignment for Spatially-Aware Vision-Language-Action Models
VEGA improves spatial reasoning in VLA models for robotics by aligning visual encoder features with 3D-supervised DINOv2 representations via a temporary projector and cosine similarity loss.
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Focusable Monocular Depth Estimation
FocusDepth is a prompt-conditioned framework that fuses SAM3 features into Depth Anything models via Multi-Scale Spatial-Aligned Fusion to improve target-region depth accuracy on the new FDE-Bench.
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SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images
SpatialForge synthesizes 10 million spatial QA pairs from in-the-wild 2D images to train VLMs for better depth ordering, layout, and viewpoint-dependent reasoning.