Segment Any 3D Object with Language: A Versatile Open-Vocabulary 3D Instance Segmentation Framework

核心概念

SOLE, a semantic and geometric-aware visual-language learning framework, can directly segment 3D objects from point clouds with strong generalizability by leveraging multimodal information and associations.

要約

The paper introduces SOLE, a framework for open-vocabulary 3D instance segmentation (OV-3DIS) that can segment 3D objects from point clouds using free-form language instructions.
Key highlights:

SOLE uses a multimodal fusion network that combines 3D backbone features with projected 2D CLIP features to capture both geometric and semantic information.
SOLE introduces a cross-modality decoder to effectively integrate textual information during the decoding process, enabling the model to respond to various language instructions.
SOLE is trained with three types of multimodal associations - mask-visual, mask-caption, and mask-entity associations - to align the 3D segmentation model with language semantics and enhance the mask quality.
SOLE outperforms previous OV-3DIS methods by a large margin on ScanNetv2, ScanNet200, and Replica benchmarks, and achieves competitive performance compared to the fully-supervised counterpart.
Extensive qualitative results demonstrate SOLE's versatility in responding to diverse language instructions, including visual questions, attribute descriptions, and functional descriptions.

統計

"In this paper, we investigate Open-Vocabulary 3D Instance Segmentation (OV-3DIS) with free-form language instructions."
"Earlier works that rely on only annotated base categories for training suffer from limited generalization to unseen novel categories."
"Recent works mitigate poor generalizability to novel categories by generating class-agnostic masks or projecting generalized masks from 2D to 3D, but disregard semantic or geometry information, leading to sub-optimal performance."

引用

"Instead, generating generalizable but semantic-related masks directly from 3D point clouds would result in superior outcomes."
"Equipped with a multimodal fusion network and three types of multimodal associations, our visual-language learning framework (SOLE) outperforms previous works by a large margin on ScanNetv2 [8], ScanNet200 [52] and Replica [56] benchmarks."
"Furthermore, SOLE can respond to free-form queries, including but not limited to questions, attributes description, and functional description (Fig. 1 and Fig. 4)."

抽出されたキーインサイト

Segment Any 3D Object with Language

by Seungjun Lee... 場所 arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.02157.pdf

深掘り質問

How can the proposed multimodal associations be extended to other 3D scene understanding tasks beyond instance segmentation, such as 3D object detection or 3D semantic segmentation

The proposed multimodal associations in the SOLE framework can be extended to other 3D scene understanding tasks beyond instance segmentation by adapting them to suit the specific requirements of tasks like 3D object detection or 3D semantic segmentation. For 3D object detection, the mask-visual association can be utilized to align visual features with object instances, enabling the model to detect and localize objects accurately. The mask-caption association can provide textual descriptions of detected objects, enhancing the interpretability of the results. Additionally, the mask-entity association can be leveraged to associate detected objects with specific semantic categories, improving the classification accuracy of the detected objects. By incorporating these multimodal associations into the respective components of 3D object detection models, such as region proposal networks and classification heads, the model can effectively integrate visual and language information for robust performance in detecting objects in 3D scenes.

What are the potential limitations of the current SOLE framework, and how could it be further improved to handle more complex and diverse language instructions

While the SOLE framework has shown promising results in open-vocabulary 3D instance segmentation, there are potential limitations that could be addressed for further improvement. One limitation is the reliance on pre-trained CLIP features for semantic information, which may not capture all nuances of 3D scenes. To overcome this limitation, integrating additional pre-trained models or domain-specific features could enhance the model's understanding of complex scenes. Another limitation is the current focus on single-instance segmentation, which may not scale well to complex scenes with multiple interacting objects. Introducing mechanisms for handling occlusions, interactions, and context awareness could improve the model's performance in such scenarios. Furthermore, the framework's response to diverse and complex language instructions could be enhanced by incorporating more advanced natural language processing techniques, such as contextual embeddings or transformer models, to better interpret and respond to nuanced language queries.

Given the success of SOLE in 3D instance segmentation, how could the visual-language learning approach be applied to other 3D perception tasks, such as 3D object manipulation or 3D scene understanding for robotics applications

The success of the SOLE framework in 3D instance segmentation opens up possibilities for applying the visual-language learning approach to other 3D perception tasks, such as 3D object manipulation or 3D scene understanding for robotics applications. In 3D object manipulation, the multimodal associations in SOLE can be adapted to facilitate the understanding of object attributes, spatial relationships, and manipulation instructions. By integrating language instructions with visual cues, robots can interpret and execute complex manipulation tasks with higher precision and efficiency. For 3D scene understanding in robotics applications, the SOLE framework can be extended to enable robots to comprehend and navigate dynamic 3D environments based on language instructions. By incorporating multimodal associations for scene interpretation, object recognition, and spatial reasoning, robots can interact with their surroundings more intelligently and autonomously, enhancing their capabilities in real-world scenarios.