NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

NumGrad-Pull is a novel method for high-fidelity surface reconstruction from unoriented and unordered 3D point clouds, developed by researchers from Australian National University and The Chinese University of Hong Kong. It introduces a hybrid explicit-implicit tri-plane representation to accelerate signed distance function learning and enhance local detail fidelity. The system employs numerical gradients instead of conventional analytical computations to improve training stability for grid-based tri-planes. Additionally, NumGrad-Pull features a progressive plane expansion strategy, starting with an 8x8 resolution and expanding to 32x32 over three stages (3k, 8k, 12k iterations), for faster SDF convergence, and a data sampling strategy to mitigate reconstruction artifacts. Experiments on benchmarks like ABC, FAMOUS, and ShapeNet demonstrate its effectiveness, outperforming state-of-the-art methods by margins of 0.02 to 0.04 CD on ABC/FAMOUS and 0.012 CD on ShapeNet, while achieving 1.8x to 5.4x speedups.

Key takeaway

For Computer Vision Engineers developing 3D reconstruction pipelines, NumGrad-Pull offers a significant advancement in generating high-fidelity surfaces from unoriented point clouds. You should consider integrating its hybrid tri-plane representation and numerical gradient approach to achieve superior detail capture and faster query speeds compared to traditional implicit methods. This can streamline your 3D content creation workflows and improve the conversion of raw scan data into production-ready models.

Key insights

NumGrad-Pull uses numerical gradients with a hybrid tri-plane SDF for fast, high-fidelity surface reconstruction from point clouds.

Principles

• Hybrid explicit-implicit representations balance speed and detail.
• Numerical gradients stabilize grid-based neural SDF training.
• Progressive resolution expansion aids convergence and detail capture.

Method

NumGrad-Pull parameterizes an SDF with a tri-plane structure and shallow MLP. It uses finite differentiation for numerical gradients, a progressive tri-plane expansion scheme, and a complementary query location sampling strategy.

In practice

• Apply hybrid tri-plane SDFs for faster 3D query speeds.
• Implement numerical gradients to stabilize grid-based neural network training.
• Use progressive resolution schemes for coarse-to-fine model optimization.

Topics

• Surface Reconstruction
• Point Clouds
• Signed Distance Functions
• Tri-plane Representation
• Numerical Gradients
• Neural Networks
• 3D Content Creation

Code references

• CuiRuikai/NumGrad-Pull

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.