Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer (2411.07899v3)

Abstract: The evolution of 3D visualization techniques has fundamentally transformed how we interact with digital content. At the forefront of this change is point cloud technology, offering an immersive experience that surpasses traditional 2D representations. However, the massive data size of point clouds presents significant challenges in data compression. Current methods for lossy point cloud attribute compression (PCAC) generally focus on reconstructing the original point clouds with minimal error. However, for point cloud visualization scenarios, the reconstructed point clouds with distortion still need to undergo a complex rendering process, which affects the final user-perceived quality. In this paper, we propose an end-to-end deep learning framework that seamlessly integrates PCAC with differentiable rendering, denoted as rendering-oriented PCAC (RO-PCAC), directly targeting the quality of rendered multiview images for viewing. In a differentiable manner, the impact of the rendering process on the reconstructed point clouds is taken into account. Moreover, we characterize point clouds as sparse tensors and propose a sparse tensor-based transformer, called SP-Trans. By aligning with the local density of the point cloud and utilizing an enhanced local attention mechanism, SP-Trans captures the intricate relationships within the point cloud, further improving feature analysis and synthesis within the framework. Extensive experiments demonstrate that the proposed RO-PCAC achieves state-of-the-art compression performance, compared to existing reconstruction-oriented methods, including traditional, learning-based, and hybrid methods.

• The paper proposes RO-PCAC, a novel framework integrating differentiable rendering and a Sparse Tensor-based Transformer (SP-Trans) to optimize point cloud attribute compression based on rendered image quality rather than raw data fidelity.
• RO-PCAC utilizes a differentiable rendering module for gradient optimization and SP-Trans with local self-attention for efficient processing of sparse point cloud data, capturing intricate inter-point dependencies.
• Experimental results show RO-PCAC outperforms state-of-the-art methods like G-PCC, achieving superior compression efficiency and retaining better texture detail in rendered images on benchmark datasets.

Insightful Overview of "Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer"

The paper "Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer" presents a novel approach to point cloud attribute compression, which uniquely integrates rendering considerations directly into the compression framework. The authors emphasize the importance of aligning compression objectives with the perceptual qualities of the final rendered multiview images, asserting that traditional methods often overlook this aspect.

The core innovation in this work is the introduction of a rendering-oriented point cloud attribute compression (RO-PCAC) framework. Distinct from conventional approaches that primarily focus on minimizing reconstruction errors after decompression, RO-PCAC optimizes the quality of the rendered multiview images from the reconstructed point clouds. This shift recognizes that, in many practical applications such as virtual reality, enhanced user experience is ultimately determined by the rendered imagery rather than the fidelity of the reconstructed raw data.

To achieve this paradigm shift, the framework incorporates a differentiable rendering module that permits gradient-based optimization of rendering effects during training. This ensures the compression model is acutely aware of how different compression strategies impact the visual quality of images generated from the point clouds. The paper further augments the compression architecture with a Sparse Tensor-based Transformer (SP-Trans), which leverages sparse tensor representations to efficiently capture intricate inter-point dependencies and local geometric features in point clouds. The SP-Trans model, characterized by local self-attention mechanisms, adapts to the unique sparsity of point cloud data, markedly enhancing the compression process.

Experimental results showcase the RO-PCAC’s superior performance in rendering-oriented compression contexts. When benchmarked against state-of-the-art methods like G-PCC v14, G-PCC v23, SparsePCAC, and ScalablePCAC, RO-PCAC demonstrates significant improvements as evidenced by reductions in BD-BR (Bjøntegaard Delta Rate) and enhanced PSNR and MS-SSIM metrics across widely recognized datasets such as 8i Voxelized Full Bodies and Owlii. The proposed method not only achieves higher compression efficiency but also retains more texture detail in rendered images, substantiating the effectiveness of its rendering-oriented strategy.

The speculative implications of this work are manifold. On a practical level, the integration of rendering considerations could dramatically enhance applications in virtual and augmented reality, where rendering quality is paramount. Theoretically, this work suggests a new direction for compression research where traditional fidelity measures might be supplanted or augmented by perceptual and application-specific metrics. The introduction of SP-Trans further emphasizes the potential for sparse data structures and transformer architectures to redefine how we approach multi-dimensional data compression.

Looking forward, this research could inspire the adaptation of similar rendering-oriented approaches across various domains where visualization is the end-goal of data processing. Moreover, future developments may focus on optimizing the computational complexity of such frameworks, ensuring they can be implemented in real-time scenarios while maintaining their superior compression and rendering qualities.