MIF integrates appearance, spatial, and geometry fields with discrepancy detection to raise humanoid relocation success from 12% to 94% in dynamic offices while cutting memory use by 91.4%.
Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1):99– 106, 2021
3 Pith papers cite this work. Polarity classification is still indexing.
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ShapeGen generates shape-diverse 3D robotic manipulation demonstrations without simulators by curating a functional shape library and applying a minimal-annotation pipeline for novel, physically plausible data.
TACO reformulates neural implicit mapping as temporal consensus optimization to enable continual adaptation to scene changes without data replay or storage.
citing papers explorer
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Learning to Evolve: Multi-modal Interactive Fields for Robust Humanoid Navigation in Dynamic Environments
MIF integrates appearance, spatial, and geometry fields with discrepancy detection to raise humanoid relocation success from 12% to 94% in dynamic offices while cutting memory use by 91.4%.
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ShapeGen: Robotic Data Generation for Category-Level Manipulation
ShapeGen generates shape-diverse 3D robotic manipulation demonstrations without simulators by curating a functional shape library and applying a minimal-annotation pipeline for novel, physically plausible data.
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TACO: Temporal Consensus Optimization for Continual Neural Mapping
TACO reformulates neural implicit mapping as temporal consensus optimization to enable continual adaptation to scene changes without data replay or storage.