Surface Reconstruction from Point Clouds via Grid-based Intersection Prediction

核心概念

Novel approach predicts intersection points for improved surface reconstruction.

摘要

• Introduction
  • Surface reconstruction from point clouds is crucial in computer vision and graphics.
  • Traditional methods like Poisson Surface Reconstruction have limitations.
• SDF-based vs. UDF-based Methods
  • SDF excels at smooth meshes but struggles with open surfaces.
  • UDF can represent open surfaces but introduces noise near the surface.
• Proposed Approach
  • Directly predicts intersection points between line segments and implicit surfaces.
  • Improves reconstruction quality and eliminates artifacts in the mesh.
• Methodology
  • Utilizes Marching Cubes for mesh generation.
  • Importance of accurate sign prediction and intersection positions highlighted.
• Experiments
  • Outperforms existing methods on ShapeNet, MGN, and ScanNet datasets.
• Ablation Study
  • Sign module and intersection module significantly impact surface quality.

統計資料

|P1Q|
|P2Q|
α = |P1Q| / |P1P2|

引述

"Our approach demonstrates state-of-the-art performance on three datasets: ShapeNet, MGN, and ScanNet."
"Accuracy of intersection positions significantly impacts the quality of surface reconstruction."

從以下內容提煉的關鍵洞見

Surface Reconstruction from Point Clouds via Grid-based Intersection Prediction

by Hui Tian,Kai... 於 arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14085.pdf

深入探究

How does predicting intersection points directly improve surface continuity

Predicting intersection points directly improves surface continuity by ensuring that the reconstructed mesh maintains a smooth and continuous appearance. By accurately predicting the exact position of intersection points between sampled line segments of point pairs and implicit surfaces, this method eliminates artifacts in the mesh that may arise from incorrect intersection positions. This accuracy in predicting intersections helps create a more seamless transition between different parts of the surface, resulting in a visually appealing and cohesive reconstruction.

What are the implications of introducing noise near the surface in UDF-based methods

Introducing noise near the surface in UDF-based methods can have significant implications on the quality of surface reconstruction. The discontinuity of the UDF gradient near the surface leads to challenges in accurately predicting sharp changes, which can result in noisy representations close to the zero-level set. This noise introduces inaccuracies when determining intersection points between cube edges and surfaces, ultimately leading to artifacts in the reconstructed mesh. While UDF-based methods excel at representing open surfaces, this noise near the surface can impact overall mesh quality by introducing errors and inconsistencies.

How can this method be applied to real-world scenarios beyond academic datasets

This method has practical applications beyond academic datasets and can be valuable in real-world scenarios such as 3D modeling for virtual reality environments, architectural design, medical imaging, or industrial prototyping. In virtual reality applications, accurate surface reconstruction is crucial for creating realistic immersive experiences with detailed objects and environments. Architectural design firms could use this method for precise modeling of structures based on point cloud data captured during site surveys or building scans. In medical imaging, it could aid in reconstructing anatomical structures from scanned data for surgical planning or research purposes. Industrial prototyping could benefit from accurate surface reconstructions for designing complex components or machinery based on scanned point clouds.