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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SadilKhan/MARVEL-FX3D
공식 코드MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
https://github.com/SxJyJay/MORE
공식 코드MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes
https://github.com/ch3cook-fdu/vote2cap-detr
공식 코드https://github.com/curryyuan/x-trans2cap
공식 코드X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning
https://github.com/embodied-generalist/embodied-generalist
공식 코드https://github.com/heng-hw/spacap3d
공식 코드Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds
https://github.com/jxbbb/tod3cap
공식 코드https://github.com/leon1207/3dgctr
공식 코드https://github.com/open3da/ll3da
공식 코드LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning
https://huggingface.co/datasets/sankalpsinha77/MARVEL-40M
공식 코드MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
https://github.com/leolyj/3d-vlp
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.