SemanticBridge provides a new 3D dataset for bridge component segmentation and quantifies sensor-induced domain gaps that drop model performance by up to 11.4% mIoU.
Point transformer v3: Simpler, faster, stronger.arXiv preprint arXiv:2312.10035, 2023
4 Pith papers cite this work, alongside 21 external citations. Polarity classification is still indexing.
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FLORA is an octree-based deep learning framework with auxiliary data fusion that predicts forest attributes from heterogeneous LiDAR, achieving rRMSE of 12.3% for dominant height and 39% for total volume on 32k French NFI plots.
A graph-structured framework fuses 3D perception with rule-based, LLM, and memory reasoning to raise hazard coverage from 57% to 93% across 115 simulated underground mine scenarios.
HAS-KD combines information-oriented heterogeneous distillation from multi-modal models with adept snapshot distillation from training checkpoints to reach SOTA 3D semantic segmentation on ScanNetV2 and S3DIS without added inference burden.
citing papers explorer
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SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis
SemanticBridge provides a new 3D dataset for bridge component segmentation and quantifies sensor-induced domain gaps that drop model performance by up to 11.4% mIoU.
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FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
FLORA is an octree-based deep learning framework with auxiliary data fusion that predicts forest attributes from heterogeneous LiDAR, achieving rRMSE of 12.3% for dominant height and 39% for total volume on 32k French NFI plots.
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From 3D Perception to Safety Reasoning: A Graph-Based Framework for Real-Time Underground Mine Monitoring
A graph-structured framework fuses 3D perception with rule-based, LLM, and memory reasoning to raise hazard coverage from 57% to 93% across 115 simulated underground mine scenarios.
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Heterogeneous and Adept Snapshot Distillation for 3D Semantic Segmentation
HAS-KD combines information-oriented heterogeneous distillation from multi-modal models with adept snapshot distillation from training checkpoints to reach SOTA 3D semantic segmentation on ScanNetV2 and S3DIS without added inference burden.