LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding (2412.17635v2)

Abstract: Applying Gaussian Splatting to perception tasks for 3D scene understanding is becoming increasingly popular. Most existing works primarily focus on rendering 2D feature maps from novel viewpoints, which leads to an imprecise 3D language field with outlier languages, ultimately failing to align objects in 3D space. By utilizing masked images for feature extraction, these approaches also lack essential contextual information, leading to inaccurate feature representation. To this end, we propose a Language-Embedded Surface Field (LangSurf), which accurately aligns the 3D language fields with the surface of objects, facilitating precise 2D and 3D segmentation with text query, widely expanding the downstream tasks such as removal and editing. The core of LangSurf is a joint training strategy that flattens the language Gaussian on the object surfaces using geometry supervision and contrastive losses to assign accurate language features to the Gaussians of objects. In addition, we also introduce the Hierarchical-Context Awareness Module to extract features at the image level for contextual information then perform hierarchical mask pooling using masks segmented by SAM to obtain fine-grained language features in different hierarchies. Extensive experiments on open-vocabulary 2D and 3D semantic segmentation demonstrate that LangSurf outperforms the previous state-of-the-art method LangSplat by a large margin. As shown in Fig. 1, our method is capable of segmenting objects in 3D space, thus boosting the effectiveness of our approach in instance recognition, removal, and editing, which is also supported by comprehensive experiments. \url{https://langsurf.github.io}.

• The paper introduces LangSurf, a novel model that embeds language into 3D object surfaces using a Language-Embedded Surface Field and joint training to improve semantic scene understanding.
• LangSurf achieves substantial performance gains over existing methods on LERF and ScanNet datasets, including a 25.11% enhancement in Semantic F-Score for 3D segmentation and significant open-vocabulary segmentation improvements.
• The model utilizes a Hierarchical-Context Awareness Module and self-supervised semantic grouping for robust feature extraction and instance awareness, enabling effective 3D object editing and practical applications in VR and robotics.

Insights into "LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding"

The paper "LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding" presents a novel approach towards improving 3D scene understanding by effectively embedding semantic information within the 3D space of object surfaces. The proposed model, LangSurf, addresses limitations in prior methods which struggled to provide precise language feature alignment with 3D object surfaces owing to inadequate contextual information and emphasis on 2D rendering.

LangSurf distinguishes itself from previous frameworks like LangSplat by implementing a Language-Embedded Surface Field. This strategy enhances the spatial coherence of the semantic field in 3D space. To accomplish this, LangSurf adopts a joint training method that integrates geometry supervision and contrastive losses, ensuring semantic features accurately adhere to object surfaces. This structuring is pivotal for a range of applications, from semantic and instance segmentation in 3D, to queries and object editing/removal tasks. Specifically, the paper introduces a Hierarchical-Context Awareness Module, which enriches semantic feature extraction by leveraging contextual information, particularly benefiting low-texture regions or complex structures.

The paper reports a substantial performance improvement over existing methods, notably LangSplat, through extensive experimentation on the LERF and ScanNet datasets. Noteworthy results include a significant advancement, sometimes exceeding 10%, in open-vocabulary 2D and 3D semantic segmentation tasks. These enhancements are backed by numerical evaluations presented in tables, highlighting LangSurf's superior mIoU and mAcc metrics. Furthermore, LangSurf's capabilities extend into robust 3D object editing and removal applications, underscoring its versatility and effectiveness.

A key contribution of LangSurf is the implementation of a self-supervised semantic grouping strategy paired with instance-aware training. This methodology ensures semantic distinctions are maintained between object instances, enhancing the accuracy of semantic fields in 3D space. The paper showcases significant improvements, particularly a 25.11% enhancement in Semantic F-Score over competitors on 3D segmentation tasks.

The implications of this research are considerable. Practically, LangSurf facilitates more effective human-computer interactions in domains such as virtual reality and robotics, among others. Theoretically, the method provides enhanced spatial semantic understanding by intertwining language with object surfaces, thus paving the way for future developments in intuitive scene comprehension and manipulation.

Future directions might explore further efficiency in aligning semantic fields dynamically or enhancing performance across unevenly distributed datasets. While the proposed method shows impressive capabilities in various downstream tasks, addressing the challenges inherent in complex datasets and object diversity remains an open area for further research.

In essence, LangSurf represents a thoughtful advancement in embedding language within 3D scene understanding, underpinned by rigorous methodology and demonstrable empirical improvements. The research lays a robust foundation for future work in language-integrated scene comprehension, promising enriched interactions across digital scenes and environments.