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3D Open-Vocabulary Instance Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Open-vocabulary 3D instance segmentation is a computer vision task that involves identifying and delineating individual objects or instances within a three-dimensional (3D) scene without prior knowledge of a fixed set of object classes or categories. In other words, it extends traditional instance segmentation to a scenario where the number and types of objects present in the 3D environment are not predefined or limited to a specific vocabulary.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Any3DIS: Class-Agnostic 3D Instance Segmentation by 2D Mask Tracking
Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation
OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation
MaskClustering: View Consensus based Mask Graph Clustering for Open-Vocabulary 3D Instance Segmentation
Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance
OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data
OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding
OpenMask3D: Open-Vocabulary 3D Instance Segmentation
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
OpenScene: 3D Scene Understanding with Open Vocabularies
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
PointCLIP: Point Cloud Understanding by CLIP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.