Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points (2209.09510v1)

Abstract: Poisson surface reconstruction (PSR) remains a popular technique for reconstructing watertight surfaces from 3D point samples thanks to its efficiency, simplicity, and robustness. Yet, the existing PSR method and subsequent variants work only for oriented points. This paper intends to validate that an improved PSR, called iPSR, can completely eliminate the requirement of point normals and proceed in an iterative manner. In each iteration, iPSR takes as input point samples with normals directly computed from the surface obtained in the preceding iteration, and then generates a new surface with better quality. Extensive quantitative evaluation confirms that the new iPSR algorithm converges in 5-30 iterations even with randomly initialized normals. If initialized with a simple visibility based heuristic, iPSR can further reduce the number of iterations. We conduct comprehensive comparisons with PSR and other powerful implicit-function based methods. Finally, we confirm iPSR's effectiveness and scalability on the AIM@SHAPE dataset and challenging (indoor and outdoor) scenes. Code and data for this paper are at https://github.com/houfei0801/ipsr.

• The paper introduces an iterative enhancement to Poisson surface reconstruction that recovers watertight surfaces from unoriented points.
• iPSR iteratively refines normals, converging typically in 5 to 30 iterations—with about 10 iterations on average using random initialization—and is accelerated by visibility heuristics.
• The method is robust to noise, outliers, and non-uniform sampling, broadening its applicability to Lidar scans, RGBD imagery, and large-scale datasets.

Essay on "Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points"

The paper "Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points" presents a significant advancement in the field of 3D surface reconstruction by introducing an enhanced version of the Poisson surface reconstruction (PSR) method, termed iPSR. Traditionally, PSR has been a favored approach for reconstructing watertight surfaces from 3D point samples, primarily due to its computational efficiency, simplicity, and robustness. However, a notable limitation of the classic PSR and its variants has been the requirement for oriented input points, which constrains its application in scenarios where normal orientation information is unavailable or inaccurate.

iPSR addresses this limitation by introducing a novel capability: it can recover surfaces from unoriented points through an iterative refinement process that gradually reconstructs the surface while simultaneously updating and refining point normals. This iterative approach leverages the inherent properties of Poisson's equation, iteratively solving the screened version to update the surface model. The process starts by initializing normals, which can be randomly assigned or estimated based on simple heuristics like visibility from predefined viewpoints. In each iterative cycle, normals are recalculated using information derived from the previously reconstructed surface, improving orientation accuracy progressively.

Quantitative evaluation indicates that iPSR effectively results in closed, watertight surfaces after a convergence period spanning 5 to 30 iterations, with a reported average of about 10 iterations when initialized with random normals. Furthermore, employing a visibility-based normal initialization heuristic reduces the number of iterations required, indicating higher computational efficiency and potentially faster convergence rates. Importantly, the work demonstrates iPSR's applicability and scalability on expansive datasets, such as the AIM@SHAPE dataset, showing resilience to noise, outliers, and non-uniform sampling which are common challenges in real-world data acquisition.

The significance of iPSR lies in its potential to broaden the scope of automated 3D reconstruction from point cloud data, catering to applications involving Lidar scans, RGBD imagery, and other scenarios where precise normal information is not readily available or is computationally expensive to calculate using other methods. This contributes to increased applicability across various domains, including computer graphics, virtual reality, and autonomous navigation systems.

While iPSR offers these enhanced capabilities, the development opens avenues for further refinement, such as addressing its current inability to preserve sharp geometric features and its dependency on iterative convergence for constructing the desired surface. Future work could explore hybrid methodologies that incorporate machine learning for smarter initialization or adaptive iteration strategies that further accelerate the convergence and improve robustness against topological artifacts.

This paper exemplifies how building upon well-established methods with innovative modifications like iterativity and heuristic initializations can effectively address the issues faced in existing approaches, thereby broadening their applicability and enhancing their performance in practical scenarios. The implications of this research potentially extend to wider fields within computational geometry and 3D data processing, foreshadowing more autonomously operated systems in spatial data handling.