Learning Instance-Aware Correspondences for Robust Multi-Instance Point Cloud Registration in Cluttered Scenes

핵심 개념

A coarse-to-fine approach to extract instance-aware correspondences for robust multi-instance point cloud registration, which can effectively handle cluttered scenes and heavily-occluded instances.

초록

The paper proposes MIRETR, a coarse-to-fine approach to multi-instance point cloud registration. At the coarse level, it jointly learns instance-aware superpoint features and predicts per-instance masks using an Instance-aware Geometric Transformer module. This allows the method to minimize the influence from the background and other instances, leading to reliable superpoint correspondences.

At the fine level, the superpoint correspondences are extended to instance candidates based on the instance masks. Instance-wise point correspondences are then extracted within each instance candidate to estimate per-instance poses. An efficient candidate selection and refinement algorithm is further devised to obtain the final registrations, bypassing the need for multi-model fitting.

Extensive experiments on three public benchmarks demonstrate the efficacy of the proposed method. Compared to the state-of-the-art, MIRETR achieves significant improvements, especially on the challenging ROBI benchmark where it outperforms the previous best by 16.6 points on F1 score. The method can effectively handle cluttered scenes and heavily-occluded instances by leveraging the instance-aware correspondences.

통계

MIRETR outperforms the state-of-the-art GeoTransformer by 12 percentage points on inlier ratio (IR) on the Scan2CAD benchmark.
On the challenging ROBI benchmark, MIRETR surpasses the previous best method by 16.6 points on F1 score.
On the synthetic ShapeNet benchmark, MIRETR achieves improvements of more than 10 percentage points over GeoTransformer on all registration metrics when coupled with PointCLM and ECC.

인용구

"Benefiting from the instance-aware geometric transformer, the correspondences extracted by our method provide strong instance information, facilitating the correspondence clustering process."
"Thanks to the powerful instance-aware geometric transformer, an instance candidate can cover a relatively large portion of an instance, with few points from the background or other instances, so the poses obtained in this step have already been very accurate."

핵심 통찰 요약

Learning Instance-Aware Correspondences for Robust Multi-Instance Point Cloud Registration in Cluttered Scenes

by Zhiyuan Yu,Z... 게시일 arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04557.pdf

더 깊은 질문

How can the proposed instance-aware correspondences be extended to handle multi-modal data, such as RGB-D or RGB-only inputs, for multi-instance registration

To extend the proposed instance-aware correspondences to handle multi-modal data for multi-instance registration, such as RGB-D or RGB-only inputs, several modifications and additions can be made to the existing framework:

Feature Fusion: Incorporate additional modalities, such as RGB or RGB-D data, into the feature extraction process. This can be achieved by concatenating or fusing the features extracted from different modalities at various stages of the network architecture.

Multi-Modal Attention Mechanism: Introduce a multi-modal attention mechanism that can effectively capture the correlations between different modalities. This can help in learning more robust and discriminative features for instance-aware correspondences.

Adaptive Fusion: Implement adaptive fusion techniques that dynamically adjust the contribution of each modality based on the context of the scene. This can help in handling scenarios where one modality may provide more relevant information than others.

Data Augmentation: Augment the training data with diverse examples that include different modalities to improve the model's generalization capabilities across multi-modal inputs.

Loss Function Modification: Modify the loss functions to account for the multi-modal nature of the data, ensuring that the model optimizes for accurate instance-aware correspondences across all modalities.

By incorporating these strategies, the proposed instance-aware correspondences can be extended to effectively handle multi-modal data for robust multi-instance registration in various real-world scenarios.

What are the potential limitations of the instance-aware geometric transformer module, and how can it be further improved to handle more challenging scenarios, such as highly cluttered and dynamic scenes

The instance-aware geometric transformer module, while effective, may have some limitations when dealing with highly cluttered and dynamic scenes. Some potential limitations include:

Instance Segmentation Accuracy: The accuracy of instance masks predicted by the module may decrease in highly cluttered scenes with overlapping instances, leading to incorrect instance-aware correspondences.

Robustness to Dynamic Environments: The module may struggle to adapt to dynamic scenes where instances move or change positions, affecting the reliability of the predicted instance masks and correspondences.

Scalability: The module's performance may degrade when handling a large number of instances or instances with complex geometries, impacting the overall registration accuracy.

To improve the module's performance in challenging scenarios, several enhancements can be considered:

Dynamic Instance Mask Refinement: Implement a mechanism to dynamically refine instance masks based on the evolving scene dynamics, ensuring accurate instance-aware correspondences in dynamic environments.

Adaptive Context Aggregation: Develop adaptive context aggregation techniques that can adjust the level of context considered based on the clutter and complexity of the scene, enhancing the module's robustness.

Temporal Consistency: Introduce temporal consistency constraints to maintain coherence in instance-aware correspondences over consecutive frames in dynamic scenes, improving registration accuracy.

Attention Mechanism Enhancement: Enhance the attention mechanism to focus on relevant features and suppress noise from cluttered scenes, improving the quality of instance-aware correspondences.

By addressing these limitations and implementing the suggested improvements, the instance-aware geometric transformer module can be further optimized to handle highly cluttered and dynamic scenes more effectively.

Given the strong performance of MIRETR on synthetic and indoor datasets, how can the method be adapted to work effectively on real-world industrial datasets with diverse object geometries and challenging environmental conditions

Adapting MIRETR to real-world industrial datasets with diverse object geometries and challenging environmental conditions requires several considerations and modifications:

Dataset Augmentation: Augment the training data with a wide variety of industrial objects, diverse geometries, and challenging environmental conditions to improve the model's generalization capabilities.

Domain Adaptation: Implement domain adaptation techniques to fine-tune the model on real-world industrial datasets, ensuring that it can effectively handle the specific characteristics of industrial scenes.

Robust Feature Extraction: Enhance the feature extraction process to capture intricate details and geometric variations present in industrial objects, improving the accuracy of instance-aware correspondences.

Dynamic Scene Handling: Develop mechanisms to handle dynamic industrial environments where objects may move or change positions, ensuring the model's robustness in such scenarios.

Noise and Occlusion Handling: Incorporate strategies to mitigate noise, occlusions, and clutter commonly found in industrial settings, enabling the model to extract accurate correspondences even in challenging conditions.

By incorporating these adaptations and enhancements, MIRETR can be tailored to effectively work on real-world industrial datasets with diverse object geometries and challenging environmental conditions, providing reliable multi-instance registration capabilities.