MSP-MVS: Multi-Granularity Segmentation Prior Guided Multi-View Stereo (2407.19323v4)

Abstract: Recently, patch deformation-based methods have demonstrated significant strength in multi-view stereo by adaptively expanding the reception field of patches to help reconstruct textureless areas. However, such methods mainly concentrate on searching for pixels without matching ambiguity (i.e., reliable pixels) when constructing deformed patches, while neglecting the deformation instability caused by unexpected edge-skipping, resulting in potential matching distortions. Addressing this, we propose MSP-MVS, a method introducing multi-granularity segmentation prior for edge-confined patch deformation. Specifically, to avoid unexpected edge-skipping, we first aggregate and further refine multi-granularity depth edges gained from Semantic-SAM as prior to guide patch deformation within depth-continuous (i.e., homogeneous) areas. Moreover, to address attention imbalance caused by edge-confined patch deformation, we implement adaptive equidistribution and disassemble-clustering of correlative reliable pixels (i.e., anchors), thereby promoting attention-consistent patch deformation. Finally, to prevent deformed patches from falling into local-minimum matching costs caused by the fixed sampling pattern, we introduce disparity-sampling synergistic 3D optimization to help identify global-minimum matching costs. Evaluations on ETH3D and Tanks & Temples benchmarks prove our method obtains state-of-the-art performance with remarkable generalization.

• The paper introduces a Multi-granularity Segmentation Prior that constrains patch deformation to enhance depth reconstruction in textureless regions.
• The approach achieves state-of-the-art results on ETH3D and Tanks & Temples using anchor equidistribution and iterative local search optimization.
• Methodological innovations including Semantic-SAM integration and CRF edge correction enable uniform anchor placement and improved computational efficiency.

Multi-granularity Segmentation Prior Guided Multi-View Stereo (MSP-MVS): An Overview

The reconstruction of textureless areas remains a significant issue in Multi-View Stereo (MVS), primarily due to the lack of reliable pixel correspondences within fixed patches. The paper "MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo" addresses this challenge by introducing an innovative approach that integrates multi-granularity segmentation priors into the MVS process, demonstrating an advancement in the depth reconstruction of textureless regions.

Summary of Contributions

The main contribution of this paper is the introduction of Multi-granularity Segmentation Prior (MSP) to guide patch deformation in MVS. Through the use of Semantic-SAM, the authors propose the extraction of multi-granularity depth edges to constrain patch deformation within homogeneous areas, addressing issues where traditional methods struggle with accuracy. The paper also introduces anchor equidistribution, ensuring more uniformly distributed anchors within deformed patches, enhancing the effective coverage of homogeneous regions. Furthermore, the iterative local search (ILS) optimization is incorporated to maximize the expressive capacity of each patch by representing larger patches with sparse representative candidates.

Key Results and Methods

The paper reports state-of-the-art performance on the ETH3D and Tanks & Temples (TNT) benchmarks, particularly in textureless scenes, which signifies the practical relevance of the proposed MSP-MVS method. It achieves the highest F1 score and completeness on the ETH3D dataset and the TNT Intermediate dataset. The integration of multi-granularity SAM and CRF for edge correction refines segmentation, enhancing the accuracy of anchor selection. Additionally, the proposed anchor equidistribution method prevents the wastage of reliable pixels, by employing sector averaging and anchor clustering techniques.

Methodological Innovations

1. Multi-granularity Segmentation Prior: The employment of multi-granularity SAM allows for the extraction and integration of depth edges as constraints, optimizing the positioning of anchors within homogeneous areas, thereby reducing ambiguity in textureless regions.
2. Anchor Equidistribution: This ensures that patches have a more comprehensive and uniform coverage of homogeneous areas, mitigating issues arising from traditionally fixed sector division methods.
3. Iterative Local Search Optimization: This marks a departure from fixed candidate selection, by dynamically optimizing candidate choices for better depth estimation, demonstrating improved computational efficiency.

Implications and Future Directions

The MSP-MVS framework presents notable implications for MVS applications, such as autonomous driving, augmented reality, and large-scale 3D reconstruction. By enhancing the interpretability and robustness of reconstruction in textureless areas, MSP-MVS could facilitate more reliable 3D modeling for complex environments. Future research could possibly leverage Semantic-SAM for extracting diverse 3D instances from point clouds, thereby expanding its utility in object-level scene editing and interactivity, broadening the applicability of MVS in real-world scenarios.

The MSP-MVS approach stands as a significant step forward in overcoming challenges associated with textureless areas in MVS, providing a promising direction for future advancements in 3D scene reconstruction methodologies.