PLGS: Robust Panoptic Lifting with 3D Gaussian Splatting

TL;DR

PLGS introduces a fast, robust 3D Gaussian splatting method for panoptic segmentation that effectively handles noisy 2D masks, outperforming NeRF-based approaches in quality and efficiency.

Contribution

The paper proposes a novel structured 3D Gaussian model with noise reduction and self-training strategies for panoptic segmentation, enhancing robustness and speed over existing methods.

Findings

Outperforms state-of-the-art in segmentation quality

Maintains superior efficiency compared to NeRF-based methods

Effectively handles noisy 2D supervision

Abstract

Previous methods utilize the Neural Radiance Field (NeRF) for panoptic lifting, while their training and rendering speed are unsatisfactory. In contrast, 3D Gaussian Splatting (3DGS) has emerged as a prominent technique due to its rapid training and rendering speed. However, unlike NeRF, the conventional 3DGS may not satisfy the basic smoothness assumption as it does not rely on any parameterized structures to render (e.g., MLPs). Consequently, the conventional 3DGS is, in nature, more susceptible to noisy 2D mask supervision. In this paper, we propose a new method called PLGS that enables 3DGS to generate consistent panoptic segmentation masks from noisy 2D segmentation masks while maintaining superior efficiency compared to NeRF-based methods. Specifically, we build a panoptic-aware structured 3D Gaussian model to introduce smoothness and design effective noise reduction strategies. For the semantic field, instead of initialization with structure from motion, we construct reliable semantic anchor points to initialize the 3D Gaussians. We then use these anchor points as smooth regularization during training. Additionally, we present a self-training approach using pseudo labels generated by merging the rendered masks with the noisy masks to enhance the robustness of PLGS. For the instance field, we project the 2D instance masks into 3D space and match them with oriented bounding boxes to generate cross-view consistent instance masks for supervision. Experiments on various benchmarks demonstrate that our method outperforms previous state-of-the-art methods in terms of both segmentation quality and speed.