Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (2007.09267v2)

Abstract: We are interested in reconstructing the mesh representation of object surfaces from point clouds. Surface reconstruction is a prerequisite for downstream applications such as rendering, collision avoidance for planning, animation, etc. However, the task is challenging if the input point cloud has a low resolution, which is common in real-world scenarios (e.g., from LiDAR or Kinect sensors). Existing learning-based mesh generative methods mostly predict the surface by first building a shape embedding that is at the whole object level, a design that causes issues in generating fine-grained details and generalizing to unseen categories. Instead, we propose to leverage the input point cloud as much as possible, by only adding connectivity information to existing points. Particularly, we predict which triplets of points should form faces. Our key innovation is a surrogate of local connectivity, calculated by comparing the intrinsic/extrinsic metrics. We learn to predict this surrogate using a deep point cloud network and then feed it to an efficient post-processing module for high-quality mesh generation. We demonstrate that our method can not only preserve details, handle ambiguous structures, but also possess strong generalizability to unseen categories by experiments on synthetic and real data. The code is available at https://github.com/Colin97/Point2Mesh.

• The paper introduces a novel intrinsic-extrinsic ratio guidance to improve mesh reconstructions from low-resolution point clouds.
• It leverages a deep point cloud network and k-nearest neighbor graph to accurately predict local connectivity for mesh face selection.
• Experimental results demonstrate superior performance with enhanced F-score, Chamfer distance, and normal consistency compared to traditional methods.

Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance: An Overview

The paper presented by Liu et al. introduces a novel approach to reconstruct 3D polygonal meshes from low-resolution point clouds, a task central to many practical applications such as rendering, animation, and collision avoidance. Unlike previous methods that rely on object-level shape embeddings, often resulting in challenges with fine-grained detail and generalization across unseen categories, this paper proposes a method grounded on the intrinsic properties of the input point cloud. By doing so, it seeks to enhance the reconstruction quality by accurately predicting local connectivity.

Methodology

The authors base their approach on leveraging the intrinsic-extrinsic ratio (IER) as a measure to determine the connectivity necessary for mesh generation. Here, the intrinsic metric is a geodesic measure on the point cloud surface, whereas the extrinsic metric involves the Euclidean distances. The central hypothesis is that the IER can reveal accurate connectivity by predicting which triplets of points should form the mesh faces. The research incorporates a deep point cloud network to predict this surrogate metric effectively. It then uses a post-processing algorithm to integrate these predictions into high-quality mesh reconstructions.

The process begins by assuming the point cloud to form a k-nearest neighbor graph, proposing candidate triangle faces. A classifier, trained using a point cloud convolutional network, facilitates identifying the correct triangle faces, effectively labeling candidate faces into appropriate categories based on the IER-derived criteria. The network embarks on leveraging trained geometric semantics to predict and distinguish viable mesh elements. Once filtered, a greedy algorithm composes the final mesh by assembling the faces that comply with manifold constraints, ensuring no intersections or irregularities.

Experimental Results

In terms of performance, Liu et al.'s method demonstrates superior results across a variety of benchmarks when compared to traditional counterparts like Poisson surface reconstruction (PSR) or the ball-pivoting algorithm (BPA), and even against state-of-the-art learning-based approaches such as DeepSDF and AtlasNet. Notably, the proposed method excels in F-score, Chamfer distance, and normal consistency, delivering robust performance across diverse ShapeNet categories. The experiments further reveal the approach's resilience against variations in point cloud density and distribution, as well as its robustness to noise, validating its ground truth independent capabilities even on real scan data after some preprocessing.

Implications and Future Directions

The research outlines significant implications for the applicability of dense point cloud reconstructions in domain-specific applications. By focusing on local surface details derived from the IER, instead of a global object pose or high-level descriptors, the approach transcends object specificity, enhancing its generalization capability. This step distinguishes it as a promising avenue for applications demanding accurate mesh generation from incomplete or noisy data—prevalently found in scenarios involving LiDAR or Kinect data.

Looking forward, this paper opens several pathways. There is potential for refining noise resilience and perhaps integrating denoising processes directly within the pipeline. Additionally, exploring explicit vertex positioning to account for structural deficiencies could further enhance detail recovery, critical for industrial applications requiring precise models for simulation or production. Moreover, while the method discerns ambiguous structures adeptly, augmenting it for dynamic scenes can expand its scope, adapting real-time use cases in robotics or AR/VR.

In essence, the technique offers a more grounded and local perspective on how surface meshes are reconstructed from sparse samples, surpassing limitations of previous holistic embeddings. This intrinsic-extrinsic guidance reaffirms the importance of local geometric reasoning in mesh reconstruction and suggests a profound shift towards accuracy, detail, and adaptability.