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3D dense captioning
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Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding. Apart from coarse semantic class prediction and bounding box regression as in traditional 3D object detection, 3D dense captioning aims at producing a further and finer instance-level label of natural language description on visual appearance and spatial relations for each scene object of interest.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
3D Spatial Understanding in MLLMs: Disambiguation and Evaluation
3D Scene Graph Guided Vision-Language Pre-training
MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving
See It All: Contextualized Late Aggregation for 3D Dense Captioning
Bi-directional Contextual Attention for 3D Dense Captioning
Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based Localization
TOD3Cap: Towards 3D Dense Captioning in Outdoor Scenes
A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes
LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning
An Embodied Generalist Agent in 3D World
Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning
End-to-End 3D Dense Captioning with Vote2Cap-DETR
UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding
Contextual Modeling for 3D Dense Captioning on Point Clouds
0/1 Deep Neural Networks via Block Coordinate Descent
Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds
MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes
X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding
Scan2Cap: Context-aware Dense Captioning in RGB-D Scans
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