Efficient Retrieval of 3D Scenes Using Natural Language Descriptions

核心概念

This work proposes a method to efficiently match open-set natural language descriptions to 3D scene graph representations of environments.

要約

The paper introduces the task of "language-based scene retrieval", which aims to identify the correct 3D scene that corresponds to a given natural language description. The authors propose a method called Text2SceneGraphMatcher (Text2SGM) that learns joint embeddings between text descriptions and 3D scene graphs to determine if they match.
The key highlights are:

The authors define the "language-based scene retrieval" task, which is comparable to coarse-localization using natural language descriptions.
They develop a text-to-scene-graph dataset by pairing existing 3D scene graphs with new human-generated natural language descriptions of the scenes.
The Text2SGM method transforms text-queries into graph representations, and then learns a joint embedding model to match the text-graphs to the 3D scene graphs.
Experiments show that Text2SGM outperforms baseline methods like Text2Pos and CLIP2CLIP on both the ScanScribe dataset and a new human-annotated dataset, in terms of accurately retrieving the correct scene given a natural language description.
The proposed method is efficient, with fast inference times and low memory requirements for storing the scene graph embeddings.

統計

"The office where the two posters are, and a red chair in the corner. The office has large windows, a large table in the middle, and multiple office chairs."
"There are some random things on top of the coffee table in front of the couch. The couch also has a few pillows and items on top."
"There is a room with a foosball table somewhere."

引用

"Where am I?"
"Scene Retrieval with Language"

抽出されたキーインサイト

"Where am I?" Scene Retrieval with Language

by Jiaqi Chen,D... 場所 arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14565.pdf

深掘り質問

How can the proposed method be extended to handle larger-scale, hierarchical environments beyond individual scenes?

The proposed method can be extended to handle larger-scale, hierarchical environments by incorporating a hierarchical structure into the scene graphs. Instead of representing individual scenes, the scene graphs can be expanded to include higher-level nodes that represent entire environments such as buildings, neighborhoods, or cities. These higher-level nodes can have connections to lower-level nodes representing individual scenes within the larger environment. By structuring the scene graphs hierarchically, the model can learn to navigate and retrieve information at different levels of granularity, enabling it to handle larger-scale environments.

What are the limitations of the current approach in handling ambiguous or underspecified language descriptions?

One limitation of the current approach in handling ambiguous or underspecified language descriptions is the reliance on exact matching between text descriptions and scene graphs. Ambiguous or underspecified language descriptions may not provide enough information for a precise match, leading to potential mismatches or incorrect retrievals. Additionally, the model may struggle with interpreting nuanced language cues or context that are crucial for understanding the scene accurately. Ambiguity in language descriptions can also lead to multiple possible interpretations, making it challenging for the model to determine the correct match.

How could the learned joint embeddings between text and scene graphs be leveraged for other applications beyond scene retrieval, such as language-guided robotic manipulation or augmented reality interactions?

The learned joint embeddings between text and scene graphs can be leveraged for various applications beyond scene retrieval. For language-guided robotic manipulation, the embeddings can be used to provide instructions to robots on how to interact with objects in a given scene. By matching text descriptions to scene graphs, robots can understand and execute tasks based on natural language commands. In augmented reality interactions, the embeddings can enable seamless integration of virtual elements into real-world scenes based on textual descriptions. This can enhance user experiences by allowing users to interact with augmented content using natural language input. Additionally, the embeddings can be utilized for tasks such as scene understanding, object recognition, and spatial reasoning in a wide range of applications across robotics, augmented reality, and artificial intelligence.