[Paper Review] A Self-supervised Cascaded Refinement Network for Point Cloud Completion.

Xiaogang Wang, Marcelo H. Ang+1 more|arXiv (Cornell University)|Oct 17, 2020

3D Shape Modeling and Analysis70 references4 citations

TL;DR

This paper proposes a self-supervised cascaded refinement network (CRN) for 3D point cloud completion that generates high-fidelity, detailed shapes without requiring fully complete ground truth. By employing a coarse-to-fine refinement strategy with adversarial training on partial inputs alone, the method achieves state-of-the-art performance in both qualitative and quantitative evaluations across multiple datasets.

ABSTRACT

Point clouds are often sparse and incomplete, which imposes difficulties for real-world applications, such as 3D object classification, detection and segmentation. Existing shape completion methods tend to generate coarse shapes of objects without fine-grained details. Moreover, current approaches require fully-complete ground truth, which are difficult to obtain in real-world applications. In view of these, we propose a self-supervised object completion method, which optimizes the training procedure solely on the partial input without utilizing the fully-complete ground truth. In order to generate high-quality objects with detailed geometric structures, we propose a cascaded refinement network (CRN) with a coarse-to-fine strategy to synthesize the complete objects. Considering the local details of partial input together with the adversarial training, we are able to learn the complicated distributions of point clouds and generate the object details as realistic as possible. We verify our self-supervised method on both unsupervised and supervised experimental settings and show superior performances. Quantitative and qualitative experiments on different datasets demonstrate that our method achieves more realistic outputs compared to existing state-of-the-art approaches on the 3D point cloud completion task.

Motivation & Objective

• To address the challenge of sparse and incomplete point clouds in real-world 3D vision applications such as classification, detection, and segmentation.
• To overcome the limitation of existing methods that rely on fully-complete ground truth, which is often unavailable in practice.
• To develop a self-supervised training framework that optimizes solely on partial input data without requiring complete supervision.
• To generate high-quality, detailed 3D shapes with realistic geometric structures through a coarse-to-fine refinement process.

Proposed method

• The method employs a cascaded refinement network (CRN) that progressively refines the completion from coarse to fine stages.
• It uses a self-supervised training paradigm that relies only on partial input point clouds, eliminating the need for fully-complete ground truth.
• Local geometric details from the partial input are explicitly considered during the refinement process to preserve structural fidelity.
• Adversarial training is integrated to enhance the realism of generated point cloud details and improve distribution modeling.
• The network is trained end-to-end using a loss function that combines reconstruction and adversarial objectives on partial inputs.
• The coarse-to-fine strategy enables progressive refinement, allowing the model to learn complex point cloud distributions and generate fine-grained structures.

Experimental results

Research questions

• RQ1 Can a self-supervised method achieve high-quality 3D point cloud completion without requiring fully-complete ground truth?
• RQ2 How effective is a cascaded refinement network in generating detailed geometric structures from incomplete point clouds?
• RQ3 Can adversarial training on partial inputs alone improve the realism and fidelity of completed 3D shapes?
• RQ4 How does the proposed method compare to supervised state-of-the-art approaches in both unsupervised and supervised settings?
• RQ5 To what extent does incorporating local details from the partial input improve completion quality?

Key findings

• The proposed method achieves superior performance in both quantitative and qualitative evaluations compared to existing state-of-the-art approaches on 3D point cloud completion.
• The model generates more realistic and detailed 3D shapes than baseline methods, even without access to fully-complete ground truth during training.
• The self-supervised training strategy enables effective optimization using only partial input data, making the method practical for real-world applications.
• The cascaded refinement mechanism successfully captures and enhances fine-grained geometric details across multiple stages.
• The method demonstrates strong generalization, performing well in both unsupervised and supervised experimental settings.
• Adversarial training significantly improves the realism of generated point cloud details, as confirmed by qualitative analysis.