Weighted Point Cloud Normal Estimation

Accurate surface normals are essential for nearly every 3D geometry processing pipeline, from Poisson surface reconstruction to ambient occlusion rendering. The standard approach—fitting a plane to each point’s k-nearest neighbours via PCA—is fast but fragile: near sharp edges, neighbours from both sides of the feature contribute equally, averaging out to an erroneous normal; in noisy regions, outlier neighbours corrupt the plane fit. This paper proposes a learned weighted normal estimation scheme that addresses both failure modes. For each query point, a lightweight network predicts a scalar weight for each neighbour based on its geometric relationship to the query—distance, angle deviation from a provisional normal estimate, and local density. The weighted neighbourhood is then used for a robust plane fit that suppresses the contribution of cross-feature and outlier neighbours. The weighting network is small enough to process millions of points per second on a standard GPU. Evaluated on standard normal estimation benchmarks and presented at ICME 2023, the method reduces mean angular error over PCA baselines at edges and noisy regions while remaining competitive with significantly heavier learning-based estimators.

Problem setting

Point cloud normal estimation—computing a unit surface normal at each 3D point—is a foundational pre-processing step for surface reconstruction, rendering, and shape analysis. Standard PCA-based methods treat all neighbours equally, leading to inaccurate normals near sharp features or in the presence of noise and outliers. This paper proposes a weighted normal estimation scheme that assigns adaptive weights to each neighbouring point based on geometric consistency with the local surface model.

In the broader publication record, this work appears in IEEE International Conference on Multimedia and Expo (ICME) 2023. The visual notes below pair the paper’s original figures with a concise reading of the method, experimental setup, and reported results.

Method and visual evidence

The method works on 3D geometric observations such as point clouds, poses, correspondences, or segmented regions, then uses the proposed representation to improve robustness under noise, viewpoint change, or limited observations.

The extracted figures below show the geometric representation, network or optimization pipeline, and qualitative or quantitative results.

Method overview. This image is extracted from an embedded PDF image object on page 1, then recomposed for web display.

Representation and setup. This image is extracted from an embedded PDF image object on page 3, then recomposed for web display.

Experimental evidence. This image is extracted from an embedded PDF image object on page 4, then recomposed for web display.

Result comparison. This image is extracted from an embedded PDF image object on page 5, then recomposed for web display.

Additional visual result. This image is extracted from an embedded PDF image object on page 5, then recomposed for web display.

Results and impact

The evaluation reported in IEEE International Conference on Multimedia and Expo (ICME) 2023 uses the extracted figures above to show the method’s measurement, reconstruction, segmentation, matching, or diagnostic behavior on representative experiments. These visuals are paired with the paper’s quantitative or qualitative analysis to make the workflow easier to inspect from the homepage.

Source handling

I extracted 7 candidate image objects from paper.pdf and generated the compressed WebP figures used on this page.