UniPLV: Towards Label-Efficient Open-World 3D Scene Understanding by Regional Visual Language Supervision

TL;DR

UniPLV introduces a unified multimodal framework that leverages images, text, and point clouds to improve open-world 3D scene understanding without extensive manual annotations, achieving significant performance gains.

Contribution

The paper proposes UniPLV, a novel framework that unifies point clouds, images, and text for efficient open-world 3D scene understanding, eliminating the need for point cloud-text pair construction.

Findings

Achieves 15.6% and 14.8% improvements in semantic segmentation tasks.

Effectively aligns multimodal data through innovative distillation and matching modules.

Surpasses state-of-the-art methods in open-world 3D scene understanding.

Abstract

Open-world 3D scene understanding is a critical challenge that involves recognizing and distinguishing diverse objects and categories from 3D data, such as point clouds, without relying on manual annotations. Traditional methods struggle with this open-world task, especially due to the limitations of constructing extensive point cloud-text pairs and handling multimodal data effectively. In response to these challenges, we present UniPLV, a robust framework that unifies point clouds, images, and text within a single learning paradigm for comprehensive 3D scene understanding. UniPLV leverages images as a bridge to co-embed 3D points with pre-aligned images and text in a shared feature space, eliminating the need for labor-intensive point cloud-text pair crafting. Our framework achieves precise multimodal alignment through two innovative strategies: (i) Logit and feature distillation modules between images and point clouds to enhance feature coherence; (ii) A vision-point matching module that implicitly corrects 3D semantic predictions affected by projection inaccuracies from points to pixels. To further boost performance, we implement four task-specific losses alongside a two-stage training strategy. Extensive experiments demonstrate that UniPLV significantly surpasses state-of-the-art methods, with average improvements of 15.6% and 14.8% in semantic segmentation for Base-Annotated and Annotation-Free tasks, respectively. These results underscore UniPLV's efficacy in pushing the boundaries of open-world 3D scene understanding. We will release the code to support future research and development.