PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes (2412.03451v1)

Abstract: This paper presents PlanarSplatting, an ultra-fast and accurate surface reconstruction approach for multiview indoor images. We take the 3D planes as the main objective due to their compactness and structural expressiveness in indoor scenes, and develop an explicit optimization framework that learns to fit the expected surface of indoor scenes by splatting the 3D planes into 2.5D depth and normal maps. As our PlanarSplatting operates directly on the 3D plane primitives, it eliminates the dependencies on 2D/3D plane detection and plane matching and tracking for planar surface reconstruction. Furthermore, the essential merits of plane-based representation plus CUDA-based implementation of planar splatting functions, PlanarSplatting reconstructs an indoor scene in 3 minutes while having significantly better geometric accuracy. Thanks to our ultra-fast reconstruction speed, the largest quantitative evaluation on the ScanNet and ScanNet++ datasets over hundreds of scenes clearly demonstrated the advantages of our method. We believe that our accurate and ultrafast planar surface reconstruction method will be applied in the structured data curation for surface reconstruction in the future. The code of our CUDA implementation will be publicly available. Project page: https://icetttb.github.io/PlanarSplatting/

• The paper introduces PlanarSplatting, a novel optimization-based method for accurate planar surface reconstruction from multiview images using 3D planar primitives.
• PlanarSplatting achieves significant speed improvements, reconstructing scenes within three minutes on large datasets like ScanNet while maintaining superior geometric accuracy.
• The method seamlessly integrates with Gaussian Splatting for enhanced novel view synthesis and holds potential for real-time applications in VR, robotics, and visualization.

Insightful Overview of "PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes"

The paper "PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes" introduces a method for rapid and precise planar surface reconstruction from multiview indoor images. The authors propose an optimization-based approach that reconstructs 3D planar surfaces by utilizing 3D plane primitives, effectively bypassing the need for comprehensive 2D/3D plane detection and tracking. This focus on planar primitives leverages their inherent structural expressiveness and compactness for indoor environments.

Main Contributions

The paper presents several key contributions:

1. Novel Optimization Framework: The proposed method directly operates on 3D planar primitives, eliminating dependencies on traditional plane detection methods. This localizes optimization efforts to 2.5D depth and normal maps, streamlining the reconstruction process.
2. Efficient Implementation: By adopting a CUDA-based implementation of the planar splatting function, the method notably reconstructs a scene within three minutes, demonstrating a considerable improvement in speed without compromising geometric accuracy.
3. Complementing Novel View Synthesis: The method integrates seamlessly with Gaussian Splatting (GS) methods, enhancing rendering tasks for indoor scenes and reducing optimization times.
4. Empirical Validation: The quantitative evaluation on the ScanNet and ScanNet++ datasets comprises a broad analysis over hundreds of scenes. The method demonstrates superior performance in terms of both speed and accuracy, thereby supporting the proposed method's efficacy.

Methodology

The core of the methodology involves a differentiable rendering process facilitated by a newly introduced plane splatting function. Unlike the conventional Gaussian functions used in previous approaches, the plane splatting function contributes to sharper boundary handling, improving the geometric quality of rendered scenes. This design feature enables the method to leverage monocular cues from foundation models, such as Metric3D and Omnidata, which guide the optimization of plane parameters.

Performance and Evaluation

The experimental results convey that the proposed method excels in reconstructing extensive scene geometry with low computational demand, achieving an impressive balance of accuracy and resource utilization. It outperforms existing methods like PlanarRecon in terms of completeness and detail recovery. Furthermore, when integrated with GS-based methods, it enhances rendering quality, demonstrating its robust applicability within the domain of novel view synthesis.

Implications and Future Developments

The implications of this research are significant for real-time applications requiring rapid scene reconstruction and rendering, such as in virtual reality, robotics, and architectural visualization. By enabling accurate planar surface reconstruction without heavy pre-computation or dependency on segmentation annotations, it provides a scalable solution adaptable to various settings.

Anticipated future developments could involve extending this framework to handle complex surface geometries like curved structures or integrating machine learning components for adaptive parameter tuning. Such advancements would broaden the versatility and applicability of planar primitive-based reconstructions even further.

Overall, "PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes" makes a substantial contribution to the field by presenting a method that not only enhances the speed and accuracy of planar reconstruction but also aligns with contemporary trends in 3D graphics processing and optimization.