InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring (2103.01128v2)

Abstract: Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging. In this paper, we propose a new model, named InstanceRefer, to achieve a superior 3D visual grounding through the grounding-by-matching strategy. In practice, our model first predicts the target category from the language descriptions using a simple language classification model. Then, based on the category, our model sifts out a small number of instance candidates (usually less than 20) from the panoptic segmentation of point clouds. Thus, the non-trivial 3D visual grounding task has been effectively re-formulated as a simplified instance-matching problem, considering that instance-level candidates are more rational than the redundant 3D object proposals. Subsequently, for each candidate, we perform the multi-level contextual inference, i.e., referring from instance attribute perception, instance-to-instance relation perception, and instance-to-background global localization perception, respectively. Eventually, the most relevant candidate is selected and localized by ranking confidence scores, which are obtained by the cooperative holistic visual-language feature matching. Experiments confirm that our method outperforms previous state-of-the-arts on ScanRefer online benchmark and Nr3D/Sr3D datasets.

Overview of "InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring"

InstanceRefer is an advanced framework tailored for 3D visual grounding that targets the localization of objects within point clouds based on natural language descriptions. With the growing complexity and irregularity of 3D data, traditional 2D-based methodologies fall short in addressing these challenges. This paper addresses key issues in 3D visual grounding through an innovative approach that leverages instance-based segmentation and multi-level contextual perception.

Key Contributions

1. Refinement and Reduction of Candidate Instances:
   • Unlike previous models that produce a plethora of object proposals, InstanceRefer predicts the target category from linguistic input and filters instance candidates via panoptic segmentation. This drastically reduces the number of candidates to typically less than 20, thereby simplifying the localization task.
2. Multi-Level Perception Modules:
   • Attribute Perception (AP) Module: Extracts detailed attribute information of each candidate instance, such as color, shape, and texture.
   • Relation Perception (RP) Module: Captures spatial relationships between candidate instances.
   • Global Localization Perception (GLP) Module: Incorporates the context of the entire scene to enhance the understanding of instance locations in relation to background structures.
3. Cooperative Holistic Visual-Language Matching:
   • A sophisticated matching module integrates features derived from all three perception modules (AP, RP, GLP) with the linguistic features, leading to a more fine-grained and holistic understanding of the scene.

Experimental Validation

InstanceRefer is empirically validated on the ScanRefer dataset and achieves state-of-the-art results in both validation and benchmarking scenarios. Specifically, it demonstrates significant improvements over existing methods such as TGNN and ScanRefer.

Experimental results indicate:

• ScanRefer Benchmark Performance:
  • Unique Objects: Achieved an accuracy of 66.83% at IoU threshold 0.5.
  • Multiple Objects: Showed an accuracy of 24.77% at IoU threshold 0.5.
  • Overall: Reported an overall accuracy of 32.93% at IoU threshold 0.5.
• ReferIt3D Performance:
  • On the Nr3D and Sr3D datasets, the model also outperformed existing frameworks indicating robust generalization capabilities.

Methodology

Instance Generation: - The method utilizes panoptic segmentation to partition the point cloud into instance-level point clouds based on semantic labels.

Language Encoding: - Descriptions are encoded using GloVe embeddings and BiGRU layers, followed by attention pooling to form a global representation of the linguistic query.

Instance Matching: - Given the visual attributes (AP), spatial relations (RP), and global context (GLP), the matching module uses modular co-attention networks to derive confidence scores for candidate instances ensuring a comprehensive visual-linguistic alignment.

Implications and Future Directions

Practical Implications:

• The instance filtering mechanism significantly reduces computational overhead and improves grounding precision especially in scenes with high object density and occlusions.
• Holistic and cooperative context modeling addresses fine-grained and relational linguistic cues improving interaction with complex scene layouts.

Theoretical Implications:

• Introducing multi-level contextual referring establishes a more nuanced relationship between visual entities and their linguistic descriptions.
• The cooperative model sets a benchmark for future frameworks aiming to integrate visual and language modalities more effectively.

Future Directions:

• Deeper Contextual Modeling: Expanding perception modules to encompass temporal changes in dynamic scenes.
• Cross-Domain Generalization: Adapting and testing the framework on outdoor datasets and augmented reality setups.
• Enhanced Language Encoders: Utilizing more advanced LLMs like Transformer-based architectures for richer linguistic embeddings.

InstanceRefer marks a significant advancement in the domain of 3D visual grounding, showcasing the potential of collaborative context understanding to enhance object localization in point clouds. As the field progresses, these insights can spur further innovations in AI-driven scene understanding and human-computer interaction.