LeAP: Consistent multi-domain 3D labeling using Foundation Models
Availability of datasets is a strong driver for research on 3D semantic understanding, and whilst obtaining unlabeled 3D point cloud data is straightforward, manually annotating this data with semantic labels is time-consuming and costly. Recently, Vision Foundation Models (VFMs) enable open-set semantic segmentation on camera images, potentially aiding automatic labeling. However,VFMs for 3D data have been limited to adaptations of 2D models, which can introduce inconsistencies to 3D labels. This work introduces Label Any Pointcloud (LeAP), leveraging 2D VFMs to automatically label 3D data with any set of classes in any kind of application whilst ensuring label consistency. Using a Bayesian update, point labels are combined into voxels to improve spatio-temporal consistency. A novel 3D Consistency Network (3D-CN) exploits 3D information to further improve label quality. Through various experiments, we show that our method can generate high-quality 3D semantic labels across diverse fields without any manual labeling. Further, models adapted to new domains using our labels show up to a 34.2 mIoU increase in semantic segmentation tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Semantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
RELEAP: Reinforcement-Enhanced Label-Efficient Active Phenotyping for Electronic Health Records
Objective: Electronic health record (EHR) phenotyping often relies on noisy proxy labels, which undermine the reliability of downstream risk prediction. Active learning can reduce annotation costs, but most rely on fixed…
Reinforcement LearningActive LearningLEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks
Large Language Models (LLMs) exhibit strong informal mathematical reasoning but struggle to generate mechanically verifiable proofs in formal languages like Lean. We present LEAP, an agentic framework that enables genera…
Mathematical ReasoningInstruction FollowingBootstrapping a 4D LiDAR Annotation Tool from Video Foundation Models
Progress in 4D LiDAR segmentation is bottlenecked by data. Assigning temporally consistent labels across sparse point cloud sequences is costly and hard to scale, and every new task or domain tends to demand fresh dense …
Scene UnderstandingVideo SegmentationLearning and Editing Universal Graph Prompt Tuning via Reinforcement Learning
Early graph prompt tuning approaches relied on task-specific designs for Graph Neural Networks (GNNs), limiting their adaptability across diverse pre-training strategies. In contrast, another promising line of research h…
Reinforcement LearningMind the Gap: Bridging Thought Leap for Improved Chain-of-Thought Tuning
Large language models (LLMs) have achieved remarkable progress on mathematical tasks through Chain-of-Thought (CoT) reasoning. However, existing mathematical CoT datasets often suffer from Thought Leaps due to experts om…
Logical ReasoningMathematical Reasoning