NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields

Models trained using weakly supervised learning might outperform stateof-the-art models when the SOTA models are not trained on task specific data, but their performance is lower than Specialized labeling tools are essential for labeling vision SOTA models evaluated on evaluation data more similar to datasets, and both academic researchers and commercial their training data. Thus there is a need for tools that can entities have released such tools. Most existing labeling tools support large datasets creation in a time efficient manner (such as Segment Anything Labeling Tool [6] and Roboflow and low cost manner. We hope to contribute to solving [7]) use single images and therefore require significant this problem by introducing a labeling tool for computer human effort for annotating long sequences, use sequential vision datasets that uses the power of Neural Radiance data but have no geometric understanding so they cannot be Fields (NeRF) [5] for photorealistic rendering and geometric used for annotating 6DOF poses [8], or require depth data understanding. Because 3D Vision can take advantage of 3D to obtain geometric information [9, 10, 11, 12]. Our toolkit, consistency, labeled information about a single scene can be NeuralLabeling, operates on sequences of images and can applied to images from multiple viewpoints. This property thus be used to more rapidly label large datasets, and by works particularly well with photorealistic renderings such using depth reconstruction using NeRF [5] it does not rely as NeRF, where richly annotated data with many views is on input depth data.