Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps
This work addresses the challenge of efficient 3D segmentation for computer vision applications, offering an incremental improvement over existing methods by integrating pre-trained features into Neural Radiance Fields.
The paper tackles the problem of accelerating volumetric segmentation by reducing the need for dense semantic annotations and leveraging prior feature representations from large datasets. The result is a method that achieves higher segmentation accuracy with fewer annotations across various scenes.
Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels. While impressive, these methods still require a relatively large amount of supervision and segmenting an object can take several minutes in practice. Such systems typically only optimize their representation on the particular scene they are fitting, without leveraging any prior information from previously seen images. In this paper, we propose to use features extracted with models trained on large existing datasets to improve segmentation performance. We bake this feature representation into a Neural Radiance Field (NeRF) by volumetrically rendering feature maps and supervising on features extracted from each input image. We show that by baking this representation into the NeRF, we make the subsequent classification task much easier. Our experiments show that our method achieves higher segmentation accuracy with fewer semantic annotations than existing methods over a wide range of scenes.