ActMVS is the first monocular framework for active scene reconstruction that combines view factor graph construction with global depth optimization to generate online, globally consistent dense depth maps competitive with RGB-D methods on Replica datasets.
Cast: Component-aligned 3d scene reconstruction from an rgb image
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.
STABLE generates simulation-ready tabletop scenes by alternating a semantic LLM reasoner for task-aligned coarse layouts with a physics corrector for physical plausibility using progressive scene expansion.
The book presents principles from optimization and information theory to explain deep network architectures and enable new interpretable models.
citing papers explorer
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ActMVS: Active Scene Reconstruction with Monocular Multi-View Stereo
ActMVS is the first monocular framework for active scene reconstruction that combines view factor graph construction with global depth optimization to generate online, globally consistent dense depth maps competitive with RGB-D methods on Replica datasets.
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GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction
GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.
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STABLE: Simulation-Ready Tabletop Layout Generation via a Semantics-Physics Dual System
STABLE generates simulation-ready tabletop scenes by alternating a semantic LLM reasoner for task-aligned coarse layouts with a physics corrector for physical plausibility using progressive scene expansion.
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Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory
The book presents principles from optimization and information theory to explain deep network architectures and enable new interpretable models.