Mitigating Ambiguities in 3D Classification with Gaussian Splatting

TL;DR

This work addresses ambiguities in 3D classification from point clouds caused by sampling limitations and hard occupancy constraints. It introduces Gaussian Splatting (GS) point clouds, where each point is an anisotropic Gaussian with scale, rotation, and opacity to encode local geometry and material properties. The authors construct the first large-scale real-world GS dataset and demonstrate that GS inputs consistently improve classification accuracy and separability across a range of baselines. They analyze the contributions of opacity, scale, and rotation and discuss practical limitations and future directions for acquiring GS data.

Abstract

3D classification with point cloud input is a fundamental problem in 3D vision. However, due to the discrete nature and the insufficient material description of point cloud representations, there are ambiguities in distinguishing wire-like and flat surfaces, as well as transparent or reflective objects. To address these issues, we propose Gaussian Splatting (GS) point cloud-based 3D classification. We find that the scale and rotation coefficients in the GS point cloud help characterize surface types. Specifically, wire-like surfaces consist of multiple slender Gaussian ellipsoids, while flat surfaces are composed of a few flat Gaussian ellipsoids. Additionally, the opacity in the GS point cloud represents the transparency characteristics of objects. As a result, ambiguities in point cloud-based 3D classification can be mitigated utilizing GS point cloud as input. To verify the effectiveness of GS point cloud input, we construct the first real-world GS point cloud dataset in the community, which includes 20 categories with 200 objects in each category. Experiments not only validate the superiority of GS point cloud input, especially in distinguishing ambiguous objects, but also demonstrate the generalization ability across different classification methods.

Figures (7)

• Figure 1: Illustration of ambiguity mitigation in 3D classification using GS point cloud. Although the Bowl and the Basket have different surface types (flat vs wire-like, specular vs diffuse reflection), existing classifiers often confuse them due to similar shapes when described as traditional point cloud, resulting in wrong probability predictions. These ambiguities can be mitigated by introducing GS point cloud.
• Figure 2: Pipeline of 3D classification used throughout the paper.
• Figure 3: Comparison between point cloud and the ellipsoid representation in Gaussian Splatting (GS). These three examples illustrate how opacity, scale, and rotation of Gaussian coefficients represent object transparency and surface structure. In the left example, a series of large, flat ellipsoids with high opacity represent the flat, opaque surface of the metal box. In the middle example, adjustments in scale and rotation enable ellipsoids to follow the intricate, wire-like structure of the bamboo basket. The right example uses low-opacity, flat ellipsoids to represent the smooth, transparent surface of the glass container.
• Figure 4: Comparisons of the probabilities output from the PointNet with different inputs. From left to right: only position, position+opacity, position+scale+rotation, position+opacity+scale+rotation. For each sub-figure, the color of the $i$-th row and $j$-th col represents the probability of the $i$-th object be classified as the $j$-th one. The rightmost image is the colorbar.
• Figure 5: Comparison of the geometric similarity leading to misclassification between the helmet and the bowl (first row), and the classification probability map before and after using opacity, where red represents the correct class and blue represents other classes (second row).