TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers (2111.14600v1)

Abstract: In this paper, we present TransMVSNet, based on our exploration of feature matching in multi-view stereo (MVS). We analogize MVS back to its nature of a feature matching task and therefore propose a powerful Feature Matching Transformer (FMT) to leverage intra- (self-) and inter- (cross-) attention to aggregate long-range context information within and across images. To facilitate a better adaptation of the FMT, we leverage an Adaptive Receptive Field (ARF) module to ensure a smooth transit in scopes of features and bridge different stages with a feature pathway to pass transformed features and gradients across different scales. In addition, we apply pair-wise feature correlation to measure similarity between features, and adopt ambiguity-reducing focal loss to strengthen the supervision. To the best of our knowledge, TransMVSNet is the first attempt to leverage Transformer into the task of MVS. As a result, our method achieves state-of-the-art performance on DTU dataset, Tanks and Temples benchmark, and BlendedMVS dataset. The code of our method will be made available at https://github.com/MegviiRobot/TransMVSNet .

• The paper introduces TransMVSNet, a novel network that leverages transformer-based global context to improve multi-view stereo depth estimation.
• It employs a Feature Matching Transformer and Adaptive Receptive Field module to effectively combine local and global features in challenging scenes.
• Experimental results demonstrate superior accuracy and completeness over prior methods on DTU, Tanks and Temples, and BlendedMVS benchmarks.

Overview of TransMVSNet: A Transformer-based Approach for Multi-View Stereo

The paper "TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers" introduces an innovative approach to multi-view stereo (MVS) that leverages Transformers to enhance feature matching across multiple views. This well-articulated paper acknowledges the inherently challenging nature of MVS tasks, which involve recovering dense 3D structures from a set of calibrated images. Unlike previous methodologies that heavily relied on local feature extractions via CNNs, the proposed TransMVSNet integrates a Feature Matching Transformer (FMT) that uses both intra- and inter-attention mechanisms to effectively aggregate long-range contextual information both within and across images. This approach marks the first known attempt to integrate Transformer architectures within the domain of MVS.

The systemic refinement of this network through an Adaptive Receptive Field (ARF) module optimizes the transition between local feature extraction and global context aggregation, which is critical for high-fidelity depth estimation in regions with low texture or repetitive patterns. The paper's introduction of a progressive, coarse-to-fine volume regularization combined with an ambiguity-reducing focal loss presents a nuanced method for managing the inherent ambiguities of depth estimation, especially within non-Lambertian surfaces and occluded areas.

Methodological Insights

The methodology section of the paper outlines several key components that constitute TransMVSNet's architecture:

1. Feature Matching Transformer (FMT): The authors implement FMT to address the need for long-range global context, utilizing intra-attention for self-contextualization within each image and inter-attention for cross-image feature alignment.
2. Adaptive Receptive Field (ARF) Module: Deformable convolutions within the ARF module enhance the network's responsiveness to spatial variance, thereby ensuring smooth integration of local and global features.
3. Transformed Feature Pathway: This feature pathway is essential for propagating Transformer-derived features from lower to higher resolutions, thereby allowing for robust backpropagation across different scales.
4. Focal Loss Application: The utilization of focal loss effectively mitigates prediction ambiguities by enhancing the training focus on less certain depth predictions, a critical aspect for achieving high accuracy in MVS networks.

Numerical Results and Comparisons

The paper reports state-of-the-art performance on three notable datasets: the DTU dataset, Tanks and Temples benchmark, and the BlendedMVS dataset. On the DTU dataset, TransMVSNet obtains significant improvements in both accuracy and completeness, outperforming precedent methods such as CasMVSNet and UCS-Net. Its strong generalization capabilities are evident from its leading F-scores on the Tanks and Temples leaderboard, achieving superior results across diverse and complex scenes. Moreover, the experiments conducted on the BlendedMVS validation set underscore the model's robustness in producing high-quality depth maps.

Implications and Future Directions

TransMVSNet's introduction of Transformer architectures into the MVS domain has substantial implications for both theoretical advancements and practical applications. The global attention mechanism fundamentally redefines the potential for accurately reconstructing 3D scenes by overcoming the intrinsic limitations of local context extraction. Future developments could explore optimizing the computational efficiency of Transformer layers, potentially through compressive techniques or hybrid architectures that balance local and global feature processing. Extending this methodology to dynamic or real-time environments could further broaden the applicability of MVS networks in fields like autonomous navigation and augmented reality.

By providing a robust framework for integrating global contextual awareness in MVS tasks, TransMVSNet sets a new performance benchmark while also expanding the potential for future research initiatives aimed at further harnessing Transformer capabilities within computer vision.