AirObject: A Temporally Evolving Graph Embedding for Object Identification (2111.15150v2)

Abstract: Object encoding and identification are vital for robotic tasks such as autonomous exploration, semantic scene understanding, and re-localization. Previous approaches have attempted to either track objects or generate descriptors for object identification. However, such systems are limited to a "fixed" partial object representation from a single viewpoint. In a robot exploration setup, there is a requirement for a temporally "evolving" global object representation built as the robot observes the object from multiple viewpoints. Furthermore, given the vast distribution of unknown novel objects in the real world, the object identification process must be class-agnostic. In this context, we propose a novel temporal 3D object encoding approach, dubbed AirObject, to obtain global keypoint graph-based embeddings of objects. Specifically, the global 3D object embeddings are generated using a temporal convolutional network across structural information of multiple frames obtained from a graph attention-based encoding method. We demonstrate that AirObject achieves the state-of-the-art performance for video object identification and is robust to severe occlusion, perceptual aliasing, viewpoint shift, deformation, and scale transform, outperforming the state-of-the-art single-frame and sequential descriptors. To the best of our knowledge, AirObject is one of the first temporal object encoding methods. Source code is available at https://github.com/Nik-V9/AirObject.

• The paper introduces AirObject, a novel temporal 3D graph embedding framework that overcomes occlusions and viewpoint variations in object identification.
• It employs Delaunay triangulation with a two-layer Graph Attention Network to robustly encode evolving spatial and structural features.
• Experiments on video instance segmentation datasets demonstrate significant improvements in precision and recall, validating its effectiveness in dynamic environments.

AirObject: A Temporally Evolving Graph Embedding for Object Identification

The paper "AirObject: A Temporally Evolving Graph Embedding for Object Identification" addresses a core challenge in robotics: the need for an adaptable and robust object encoding mechanism that evolves over time as objects are observed from multiple viewpoints. Traditional methods for object recognition focus on static, single-viewpoint representations that often struggle with occlusions, perceptual aliasing, and other dynamic real-world conditions. These existing implementations are inadequate in scenarios demanding flexibility and temporal adaptability, such as autonomous robotic exploration, semantic scene understanding, and re-localization.

Global Key Contributions

The paper introduces a novel methodology termed "AirObject," which proposes a temporal $3$D object encoding approach rooted in graph-based embeddings. This method leverages temporal convolutional networks and graph attention-based encoding mechanisms to construct evolving topological representations that are class-agnostic and robust to diverse transformations. The paper emphasizes several critical features and purported advancements of the AirObject methodology:

1. Topological Graph Representations: AirObject utilizes Delaunay triangulation to construct topological graphs for each frame, encapsulating object geometry and keypoint relationships. This approach is purported to improve the robustness of object encoding by retaining spatial structural information, which is vital for overcoming perceptual aliasing and occlusion.
2. Graph Attention Encoder: This component employs a two-layer Graph Attention Network (GAT) to facilitate structured message passing between nodes representing distinctive object features. This encoding strategy underscores the positional and structural attributes of objects, ensuring sparse, distinct keypoint representation within descriptors.
3. Temporal Convolutional Network (TCN): AirObject's temporal encoding harnesses TCN to aggregate graph features across sequences, thereby focusing on evolving object structures rather than static snapshots. This dynamic accumulation allows AirObject to perform better against severe occlusions and dramatic viewpoint changes.

Numerical Results and Experimental Validation

The experimental validations underscore AirObject's superiority in performance across several video instance segmentation datasets, namely YouTube Video Instance Segmentation (YT-VIS), Unidentified Video Objects (UVO), Occluded Video Instance Segmentation (OVIS), and Tracking Any Object with Video Object Segmentation (TAO-VOS). AirObject reportedly achieves state-of-the-art results for video object identification, consistently surpassing both single-frame and sequence-based descriptors such as NetVLAD and SeqNet across various settings.

• Precision and Recall Improvements: The framework demonstrates a substantive increase in F1 scores and Precision-Recall AUC across all datasets, highlighting the model's adeptness in balancing precision and recall in complex, dynamic environments.
• Robustness to Variations: The results indicate substantial robustness against occlusions, perceptual aliasing, and viewpoint shifts, attributed to the topological graph-based temporal aggregation of features.

Implications and Future Directions

The development and validation of AirObject provide significant insights into the potential applications of temporally evolving embeddings in practical robotic tasks. The adaptability to real-world object interactions and occlusions proposed by this framework could enhance capabilities in autonomous robotics and advanced SLAM algorithms. There remain opportunities to refine and extend the applicability of AirObject; future work could explore enhancing computational efficiency, scalability to more complex scenes, and integration with other complementary object recognition tasks.

Additionally, while AirObject demonstrates commendable performance, integrating richer contextual information from adjacent objects or incorporating broader scene layouts using cross-attention mechanisms may further enhance temporal encoding. The paper marks a stride forward in the practical deployment of AI-based object identification in robotics, presenting a compelling case for further exploration in evolving graph embeddings.