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Interactive Segmentation of Radiance Fields

About

Radiance Fields (RF) are popular to represent casually-captured scenes for new view synthesis and several applications beyond it. Mixed reality on personal spaces needs understanding and manipulating scenes represented as RFs, with semantic segmentation of objects as an important step. Prior segmentation efforts show promise but don't scale to complex objects with diverse appearance. We present the ISRF method to interactively segment objects with fine structure and appearance. Nearest neighbor feature matching using distilled semantic features identifies high-confidence seed regions. Bilateral search in a joint spatio-semantic space grows the region to recover accurate segmentation. We show state-of-the-art results of segmenting objects from RFs and compositing them to another scene, changing appearance, etc., and an interactive segmentation tool that others can use. Project Page: https://rahul-goel.github.io/isrf/

Rahul Goel, Dhawal Sirikonda, Saurabh Saini, PJ Narayanan• 2022

Related benchmarks

TaskDatasetResultRank
Interactive 3D SegmentationNVOS
mIoU83.8
9
Interactive 3D SegmentationSPIn-NeRF
mIoU71.5
8
Single-object ReconstructionNVOS
mIOU70.1
6
3D Instance SegmentationNVOS
mIoU83.8
4
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