Enforcing View-Consistency in Class-Agnostic 3D Segmentation Fields

TL;DR

This paper introduces a novel method for directly learning class-agnostic 3D segmentation fields from radiance data, improving scene decomposition and object segmentation without extensive hyperparameter tuning.

Contribution

It proposes a new approach with spatial regularization and object slots to produce robust 3D segmentation directly from 2D masks, advancing scene understanding in radiance fields.

Findings

Successfully generates 3D panoptic segmentations in complex scenes.

Extracts high-quality 3D assets for virtual environments.

Demonstrates robustness to inconsistent class-agnostic masks.

Abstract

Radiance Fields have become a powerful tool for modeling 3D scenes from multiple images. However, they remain difficult to segment into semantically meaningful regions. Some methods work well using 2D semantic masks, but they generalize poorly to class-agnostic segmentations. More recent methods circumvent this issue by using contrastive learning to optimize a high-dimensional 3D feature field instead. However, recovering a segmentation then requires clustering and fine-tuning the associated hyperparameters. In contrast, we aim to identify the necessary changes in segmentation field methods to directly learn a segmentation field while being robust to inconsistent class-agnostic masks, successfully decomposing the scene into a set of objects of any class. By introducing an additional spatial regularization term and restricting the field to a limited number of competing object slots against which masks are matched, a meaningful object representation emerges that best explains the 2D supervision. Our experiments demonstrate the ability of our method to generate 3D panoptic segmentations on complex scenes, and extract high-quality 3D assets from radiance fields that can then be used in virtual 3D environments.