LEAP-NBV: Lightweight Edge Active-Perception for Foundation-Model Next-Best-View Planning

arXiv:2609.23974 · cs.AI · Submitted 2026-09-21 · Read on arXiv

cs.AI

Submitted: 2026-09-21

Updated: 2026-09-21

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 34× in this paper · explained in 1 episode
• Distillation — 29× in this paper · explained in 2 episodes
• Quantization — 22× in this paper · explained in 12 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Human Mesh Recovery — 6× in this paper
• Knowledge Distillation — 5× in this paper · explained in 11 episodes
• Active Perception — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Foundation Model — 4× in this paper · explained in 3 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Core — 2× in this paper · explained in 2 episodes
• Edge Deployment — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Quantization-Aware Training — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• On the Opportunities and Risks of Foundation Models
• Distilling the Knowledge in a Neural Network
• A White Paper on Neural Network Quantization
• OA-NBV: Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots

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