A tactile system recovers 6-DoF object pose from one contact pair by coarse-to-fine localization of point clouds on a known model followed by normal-aware SVD.
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In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp
6 Pith papers cite this work, alongside 3,944 external citations. Polarity classification is still indexing.
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TriFlow synthesizes nearest-vertex vector fields via flow-matching to generate artist-like 3D mesh topology, then extracts meshes via clustering and topology-aware QEM simplification.
A reliability-prediction network and gated fusion method are used to filter glare-corrupted depth data before it enters robot navigation costmaps, demonstrated on a RealSense-equipped mobile platform.
TACO is a replay-free continual neural-mapping method using importance-weighted consensus with past model snapshots to preserve stable geometry while revising outdated regions — validated quantitatively on static scenes, qualitatively on dynamic ones.
RoomRecon delivers a real-time mobile system for high-quality textured 3D room reconstructions that combines AR-guided imaging with generative AI texturing focused on permanent structures and claims to outperform prior methods in quality and speed.
A survey of RGB-D object detection from traditional hand-crafted features with machine learning to deep learning techniques.
citing papers explorer
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You Only Touch Once: 6-DoF Object Pose Estimation from Single Tactile Contact
A tactile system recovers 6-DoF object pose from one contact pair by coarse-to-fine localization of point clouds on a known model followed by normal-aware SVD.
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TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields
TriFlow synthesizes nearest-vertex vector fields via flow-matching to generate artist-like 3D mesh topology, then extracts meshes via clustering and topology-aware QEM simplification.
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Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps
A reliability-prediction network and gated fusion method are used to filter glare-corrupted depth data before it enters robot navigation costmaps, demonstrated on a RealSense-equipped mobile platform.
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TACO: Temporal Consensus Optimization for Continual Neural Mapping
TACO is a replay-free continual neural-mapping method using importance-weighted consensus with past model snapshots to preserve stable geometry while revising outdated regions — validated quantitatively on static scenes, qualitatively on dynamic ones.
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RoomRecon: High-Quality Textured Room Layout Reconstruction on Mobile Devices
RoomRecon delivers a real-time mobile system for high-quality textured 3D room reconstructions that combines AR-guided imaging with generative AI texturing focused on permanent structures and claims to outperform prior methods in quality and speed.
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RGB-D image-based Object Detection: from Traditional Methods to Deep Learning Techniques
A survey of RGB-D object detection from traditional hand-crafted features with machine learning to deep learning techniques.