3D-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors (2409.04013v2)

Abstract: Existing multi-view image compression methods often rely on 2D projection-based similarities between views to estimate disparities. While effective for small disparities, such as those in stereo images, these methods struggle with the more complex disparities encountered in wide-baseline multi-camera systems, commonly found in virtual reality and autonomous driving applications. To address this limitation, we propose 3D-LMVIC, a novel learning-based multi-view image compression framework that leverages 3D Gaussian Splatting to derive geometric priors for accurate disparity estimation. Furthermore, we introduce a depth map compression model to minimize geometric redundancy across views, along with a multi-view sequence ordering strategy based on a defined distance measure between views to enhance correlations between adjacent views. Experimental results demonstrate that 3D-LMVIC achieves superior performance compared to both traditional and learning-based methods. Additionally, it significantly improves disparity estimation accuracy over existing two-view approaches.

• The paper introduces 3D-GP-LMVIC, which leverages 3D Gaussian priors for precise multi-view disparity estimation and improved image compression.
• It implements a depth map compression model and a tailored sequence ordering method, significantly reducing bitrate while preserving quality.
• Experimental results demonstrate up to 63.69% BDBR reduction on MS-SSIM benchmarks, highlighting its potential for VR, AR, and autonomous driving applications.

Overview of 3D-GP-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors

The paper "3D-GP-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors" introduces a novel multi-view image compression framework that utilizes 3D Gaussian geometric priors to enhance the efficiency of image coding. This method, referred to as 3D-GP-LMVIC, addresses the challenges associated with accurately modeling correlations between views in the presence of significant view changes, a limitation of existing 2D-based disparity estimation techniques.

Methodology

The proposed approach leverages the concept of 3D Gaussian Splatting (3D-GS) to generate geometric priors from multi-view images, facilitating more accurate pixel-level correspondence across views. The method involves several key components:

1. Disparity Estimation Using 3D Gaussian Priors: By employing 3D-GS, the framework estimates depth maps for each view. These depth maps provide spatial information that aids in the accurate estimation of disparities across different views. The process ensures effective feature fusion from reference views during compression, improving the overall compression performance.
2. Depth Map Compression: Recognizing the importance of depth information in decoding, the authors introduce a depth map compression model to reduce geometric redundancy across views. The model includes a cross-view depth prediction module designed to capture geometric correlations, further enhancing compression efficiency.
3. Multi-View Sequence Ordering: To maximize correlations between adjacent views, the framework proposes an ordering method for multi-view sequences. This method defines a distance measure between view pairs and uses a greedy algorithm to order the views, ensuring significant overlap between adjacent views.

The paper provides a detailed description of the methodology and the underlying principles, including the mathematical formulations of depth and disparity estimation processes.

Experimental Evaluation

The effectiveness of 3D-GP-LMVIC is demonstrated through extensive experiments on datasets such as Tanks{content}Temples, Mip-NeRF\ 360, and Deep Blending. Key findings from the experimental results include:

• Compression Efficiency: The proposed 3D-GP-LMVIC surpasses both traditional codecs like MV-HEVC and several state-of-the-art learning-based codecs in terms of compression efficiency, as evidenced by the rate-distortion (RD) curves and Bjøntegaard Delta bitrate (BDBR) metrics. Notably, on the Tanks{content}Temples dataset, 3D-GP-LMVIC achieves a BDBR reduction of 47.48% for PSNR and 63.69% for MS-SSIM compared to MV-HEVC, illustrating significant improvements.
• Quality and Speed: The codec maintains competitive encoding and decoding speeds, with average runtimes of 0.19s and 0.18s respectively, positioning it as an efficient solution relative to its peers. This balance of speed and compression performance is attributed to the use of a streamlined network architecture and efficient entropy coding methods.

Implications and Future Directions

The integration of 3D geometric priors into the multi-view image coding process represents a meaningful advancement in the field. By addressing the limitations of 2D-based disparity estimation techniques, 3D-GP-LMVIC paves the way for more accurate and efficient multi-view image compression. The ability to effectively handle large disparities and complex scene geometries makes this approach particularly valuable for applications in virtual reality (VR), augmented reality (AR), and autonomous driving, where high-quality multi-view content is crucial.

The theoretical implications of this work lie in its potential to inspire further research into the use of geometrically-informed priors in various aspects of image processing and compression. Future research could explore extending this methodology to dynamic scenes and integrating it with real-time rendering systems. Additionally, the proposed depth map compression model and sequence ordering method can be further optimized and adapted to other applications requiring efficient handling of multi-view data.

In conclusion, the 3D-GP-LMVIC framework leverages 3D Gaussian geometric priors to significantly enhance the performance of multi-view image coding. This approach not only achieves superior compression efficiency but also maintains fast encoding and decoding speeds, making it a practical and effective solution for modern 3D-related applications. The promising results and robust methodology set a substantial foundation for future innovations in image and video compression technologies.