PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection (2303.08129v1)

Abstract: Masked Autoencoders learn strong visual representations and achieve state-of-the-art results in several independent modalities, yet very few works have addressed their capabilities in multi-modality settings. In this work, we focus on point cloud and RGB image data, two modalities that are often presented together in the real world, and explore their meaningful interactions. To improve upon the cross-modal synergy in existing works, we propose PiMAE, a self-supervised pre-training framework that promotes 3D and 2D interaction through three aspects. Specifically, we first notice the importance of masking strategies between the two sources and utilize a projection module to complementarily align the mask and visible tokens of the two modalities. Then, we utilize a well-crafted two-branch MAE pipeline with a novel shared decoder to promote cross-modality interaction in the mask tokens. Finally, we design a unique cross-modal reconstruction module to enhance representation learning for both modalities. Through extensive experiments performed on large-scale RGB-D scene understanding benchmarks (SUN RGB-D and ScannetV2), we discover it is nontrivial to interactively learn point-image features, where we greatly improve multiple 3D detectors, 2D detectors, and few-shot classifiers by 2.9%, 6.7%, and 2.4%, respectively. Code is available at https://github.com/BLVLab/PiMAE.

PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection

The paper presents a novel approach, PiMAE, a self-supervised pre-training framework designed to harness the synergy between point cloud data and RGB images for enhanced 3D object detection. Masked Autoencoders (MAEs) have been highly successful in learning strong representations across various modalities through unsupervised learning. However, their application in multi-modal settings remains underexplored. This paper focuses on addressing that gap by promoting meaningful interactions between point cloud and RGB inputs, which are often available concurrently in real-world scenarios.

Key Contributions

1. Multi-Modal Interaction: The PiMAE framework introduces fine-grained multi-modal interactions through three distinct strategies:
   • Cross-Modal Masking Strategy: By aligning mask and visible tokens of the two modalities via a projection module, the paper enhances the mutual understanding between RGB images and point clouds.
   • Two-Branch MAE Pipeline with Shared Decoder: By incorporating a shared decoder in the MAE, it facilitates better integration of the masked tokens across modalities, promoting stronger interaction and feature fusion.
   • Cross-Modal Reconstruction Module: This component is designed to enhance the learning of cross-modal representations by reconstructing both modalities, leveraging the interactions facilitated by previous strategies.
2. Extensive Empirical Validation: Through rigorous experiments on large-scale RGB-D scene understanding benchmarks such as SUN RGB-D and ScanNetV2, PiMAE demonstrates significant improvements over existing approaches in terms of 3D detection accuracy. The framework was shown to enhance the performance of various 3D detectors, 2D detectors, and few-shot classifiers by relative margins of 2.9\%, 6.7\%, and 2.4\%, respectively.
3. Joint Pre-Training with Complementary Masking: The adoption of a complementary cross-modal masking strategy allows PiMAE to excel in feature extraction by encouraging the two modalities to cover different aspects of the same scene. This mitigates the marginal performance typically observed in naive multi-modal models.

Theoretical and Practical Implications

Theoretically, PiMAE broadens the understanding of unsupervised representation learning by exemplifying how MAEs can be extended to multi-modal settings effectively. The incorporation of projection-based masking and cross-modal reconstruction modules reflects a deepened interaction between modalities, enriching representational learning.

Practically, the improvements observed in object detection suggest PiMAE's potential in real-world applications such as autonomous driving and robotics, where sensor fusion from RGB cameras and LiDAR is routine. PiMAE's ability to learn robust multi-modal representations without extensive labeled data would likely reduce annotation costs and be advantageous for applications requiring real-time processing and accuracy.

Future Prospects

Given its promising results, future work might investigate the extension of PiMAE to other modalities or tasks. Moreover, exploring the framework's adaptability in less-constrained environments, such as dynamic outdoor scenes or non-stationary objects, could offer further insights. As multi-modal datasets and pre-training frameworks continue to evolve, PiMAE's approach could inspire subsequent advancements in the field of multi-modal machine learning and computer vision.

In conclusion, PiMAE represents a significant advancement in leveraging the capabilities of MAEs for multi-modal learning, particularly in the context of 3D object detection. The demonstrated improvements emphasize the utility of interactive cross-modal representation learning, bolstering the integration of visual data modalities in AI applications.