SparseLGS: Sparse View Language Embedded Gaussian Splatting (2412.02245v2)

Abstract: Recently, several studies have combined Gaussian Splatting to obtain scene representations with language embeddings for open-vocabulary 3D scene understanding. While these methods perform well, they essentially require very dense multi-view inputs, limiting their applicability in real-world scenarios. In this work, we propose SparseLGS to address the challenge of 3D scene understanding with pose-free and sparse view input images. Our method leverages a learning-based dense stereo model to handle pose-free and sparse inputs, and a three-step region matching approach to address the multi-view semantic inconsistency problem, which is especially important for sparse inputs. Different from directly learning high-dimensional CLIP features, we extract low-dimensional information and build bijections to avoid excessive learning and storage costs. We introduce a reconstruction loss during semantic training to improve Gaussian positions and shapes. To the best of our knowledge, we are the first to address the 3D semantic field problem with sparse pose-free inputs. Experimental results show that SparseLGS achieves comparable quality when reconstructing semantic fields with fewer inputs (3-4 views) compared to previous SOTA methods with dense input. Besides, when using the same sparse input, SparseLGS leads significantly in quality and heavily improves the computation speed (5$\times$speedup). Project page: https://ustc3dv.github.io/SparseLGS

• The paper presents SparseLGS, a method that reconstructs 3D semantic fields from sparse, pose-free inputs using language embeddings and Gaussian splatting.
• It employs a robust three-step semantic alignment process and bijection feature mapping to resolve multi-view inconsistencies and reduce computational overhead.
• The approach achieves a 5x speedup with improved mIoU and mACC on LERF and 3D-OVS datasets, promising scalable applications in AR/VR and autonomous driving.

SparseLGS: Sparse View Language Embedded Gaussian Splatting

The paper "SparseLGS: Sparse View Language Embedded Gaussian Splatting" introduces an innovative approach to 3D scene understanding by leveraging sparse view inputs without relying on pre-defined camera poses. This work presents significant advancements in integrating LLMs with 3D Gaussian Splatting, thereby facilitating efficient scene representation and enabling open-vocabulary 3D semantic field queries. The authors propose a method, SparseLGS, which resolves key challenges associated with sparse input, such as multi-view semantic inconsistency and computational inefficiency.

SparseLGS stands out by successfully reconstructing 3D semantic fields from considerably fewer inputs, specifically just 3-4 views, as opposed to the dense multi-view inputs typically required by existing state-of-the-art methods. The approach achieves a remarkable 5x speedup in processing time, offering a level of performance and efficiency advantageous for real-world applications where data acquisition can be challenging.

Key Contributions and Methodology

The core contributions of the paper lie in the development of SparseLGS, which is the first of its kind to tackle 3D semantic field reconstruction using sparse, pose-free inputs. The methodology involves several novel components:

1. Learning-Based Dense Stereo Model: The use of models like MASt3R for estimating camera poses and generating initial point clouds is central to the framework. These models replace traditional methods such as COLMAP, which often fail with sparse input views.
2. Three-Step Semantic Alignment: The authors implemented a robust three-step region matching process to resolve semantic inconsistencies across different views. This process includes RoMa-based pixel matching, inconsistent mask fusion, and reprojection matching fine-tuning, ensuring accurate multi-view semantic alignment despite sparse inputs.
3. Bijection for Feature Mapping: To address the challenges of dimensionality in semantic features, the paper establishes a bijection between low-dimensional results and the original CLIP features. This technique prevents information loss and mitigates the computational and storage burdens typical of high-dimensional data processing.
4. RGB and Semantic Training Integration: By incorporating an RGB-based reconstruction loss, the method refines Gaussian positions and shapes, thus maintaining geometric consistency during semantic training. This integration is crucial for enforcing 3D consistency in the learned semantic field under sparse input conditions.

Experimental Results

Experimental validation on the LERF and 3D-OVS datasets highlights the robustness and effectiveness of the SparseLGS framework. The approach consistently outperforms existing methods in both semantic segmentation and object localization tasks, delivering higher mIoU and mACC scores. Notably, SparseLGS achieves these results while necessitating significantly fewer input views and reducing the computational time involved.

In the case of the LERF dataset, SparseLGS demonstrates superior accuracy in open-vocabulary 3D object localization and semantic segmentation. When applied to the 3D-OVS dataset, the method maintains its advantage in IoU scores, even in varied and complex scenes.

Implications and Future Directions

SparseLGS offers a scalable solution fitting for applications in autonomous driving, robotic manipulation, and AR/VR, where rapid and flexible 3D scene understanding from limited data is advantageous. The method's capability to efficiently handle sparse inputs without compromising on accuracy holds promise for advancing 3D scene representation techniques further.

The implications of these results suggest that future research might focus on enhancing the integration of LLMs with 3D scene understanding, exploring more sophisticated semantic alignment techniques, and expanding applications beyond traditional 3D reconstruction tasks. Additionally, optimizing the trade-off between input sparsity, processing speed, and output fidelity could lead to even more efficient and versatile solutions for real-time applications in dynamic environments.