Introduces object-level semantic uncertainty for VLM memory, the UQ-DAAAM refinement system, and probabilistic guarantees that selected high-quality views reduce uncertainty more effectively.
arXiv preprint arXiv:2408.04034 , year=
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2026 3representative citing papers
OpenSGA fuses vision-language, textual, and geometric features via a distance-gated attention encoder and minimum-cost-flow allocator to outperform prior methods on both frame-to-scan and subscan-to-subscan 3D scene graph alignment, backed by a new 700k-sample ScanNet-SG dataset.
FOUND-IT constructs evolving task-driven 3D scene graphs with on-demand granularity from monocular cameras by augmenting foundation models, reporting 79% higher accuracy on a grounding benchmark and real-time Jetson deployment.
citing papers explorer
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Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees
Introduces object-level semantic uncertainty for VLM memory, the UQ-DAAAM refinement system, and probabilistic guarantees that selected high-quality views reduce uncertainty more effectively.
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OpenSGA: Efficient 3D Scene Graph Alignment in the Open World
OpenSGA fuses vision-language, textual, and geometric features via a distance-gated attention encoder and minimum-cost-flow allocator to outperform prior methods on both frame-to-scan and subscan-to-subscan 3D scene graph alignment, backed by a new 700k-sample ScanNet-SG dataset.
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FOUND-IT: Foundation-model-first Task-driven 3D Scene Graphs with Granularity on Demand
FOUND-IT constructs evolving task-driven 3D scene graphs with on-demand granularity from monocular cameras by augmenting foundation models, reporting 79% higher accuracy on a grounding benchmark and real-time Jetson deployment.