Adaptive Context-Aware Multi-Modal Network for Depth Completion (2008.10833v1)

Abstract: Depth completion aims to recover a dense depth map from the sparse depth data and the corresponding single RGB image. The observed pixels provide the significant guidance for the recovery of the unobserved pixels' depth. However, due to the sparsity of the depth data, the standard convolution operation, exploited by most of existing methods, is not effective to model the observed contexts with depth values. To address this issue, we propose to adopt the graph propagation to capture the observed spatial contexts. Specifically, we first construct multiple graphs at different scales from observed pixels. Since the graph structure varies from sample to sample, we then apply the attention mechanism on the propagation, which encourages the network to model the contextual information adaptively. Furthermore, considering the mutli-modality of input data, we exploit the graph propagation on the two modalities respectively to extract multi-modal representations. Finally, we introduce the symmetric gated fusion strategy to exploit the extracted multi-modal features effectively. The proposed strategy preserves the original information for one modality and also absorbs complementary information from the other through learning the adaptive gating weights. Our model, named Adaptive Context-Aware Multi-Modal Network (ACMNet), achieves the state-of-the-art performance on two benchmarks, {\it i.e.}, KITTI and NYU-v2, and at the same time has fewer parameters than latest models. Our code is available at: \url{https://github.com/sshan-zhao/ACMNet}.

• The paper introduces ACMNet, which integrates co-attention guided graph propagation with a symmetric gated fusion module to enhance depth completion from sparse depth and RGB inputs.
• It addresses the limitations of traditional convolutional methods by adaptively modeling spatial contexts through multi-scale graphs and selective feature fusion.
• Evaluated on KITTI and NYU-v2, ACMNet achieves state-of-the-art performance with reduced parameters, paving the way for real-time applications in autonomous driving and robotics.

Overview of "Adaptive Context-Aware Multi-Modal Network for Depth Completion"

The paper presents a novel approach to the challenging problem of depth completion, which involves generating dense depth maps from sparse depth data, typically obtained from LiDAR, and corresponding RGB images. The proposed method, termed as Adaptive Context-Aware Multi-Modal Network (ACMNet), leverages a multi-modal setting to efficiently capture and exploit contextual information for accurate depth completion.

Key Contributions

The authors identify the limitations in traditional convolutional approaches for depth completion, which deal ineffectively with the sparse nature of depth data. The paper introduces several innovative strategies to overcome these limitations, emphasizing two primary advancements: the co-attention guided graph propagation (CGPM) and the symmetric gated fusion module (SGFM).

1. Co-Attention Guided Graph Propagation (CGPM): The CGPM is designed to address the challenge of modeling spatial contexts from sparse depth inputs. It employs graph propagation techniques enhanced with a co-attention mechanism to dynamically capture contextual information from observed pixels. This is achieved by constructing multi-scale graphs that vary for each sample and utilizing attention to model these contexts adaptively. The graph propagation fosters interaction between depth and RGB modalities, enhancing feature representation of unobserved pixels.
2. Symmetric Gated Fusion Module (SGFM): This module is introduced to efficiently fuse multi-modal contextual information. It employs a symmetric structure to maintain the integrity of modality-specific information while allowing cross-modality feature enhancement. This is achieved through learning adaptive gating weights that facilitate selective feature absorption between depth and RGB inputs, preserving essential information from one modality and integrating complementary data from the other.

Method Evaluation and Results

ACMNet is evaluated on two prominent benchmarks: KITTI and NYU-v2 datasets. The experimental results establish ACMNet's capability to achieve state-of-the-art performance in depth completion tasks. On the KITTI dataset, ACMNet achieves superior results while maintaining a lower number of parameters compared to existing methods. On the NYU-v2 dataset, the proposed method also demonstrates competitive performance.

Implications and Future Directions

The proposed ACMNet represents a significant step towards better handling the sparsity inherent in depth data and optimizing the fusion of multi-modal contexts. The method’s adaptability and efficiency are evident in its performance across varying levels of input sparsity. This adaptability considerably enhances its suitability for real-world applications such as autonomous driving and robotic navigation, where environmental conditions can vary widely.

Looking ahead, further investigations could explore adaptive graph structures or integration with additional sensor modalities to enhance the framework's robustness and applicability. Furthermore, the integration of ACMNet with real-time systems could present new challenges and opportunities in practical deployments.

In conclusion, this paper contributes a sophisticated approach that effectively leverages graph theory and attention mechanisms in a multi-modal context, pushing the boundaries of what can be achieved in depth completion tasks. ACMNet sets a new standard in how spatial information is aggregated and utilized from diverse sensory inputs, paving the way for more intricate and versatile computer vision systems.