DANCE: Density-agnostic and Class-aware Network for Point Cloud Completion

• URL: http://arxiv.org/abs/2511.07978v2
• Date: Mon, 17 Nov 2025 01:28:46 GMT
• Title: DANCE: Density-agnostic and Class-aware Network for Point Cloud Completion
• Authors: Da-Yeong Kim, Yeong-Jun Cho,
• Abstract summary: Point cloud completion aims to recover missing geometric structures from incomplete 3D scans.<n>DANCE is a novel framework that completes only the missing regions while preserving the observed geometry.
• Score: 1.7188280334580195
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Point cloud completion aims to recover missing geometric structures from incomplete 3D scans, which often suffer from occlusions or limited sensor viewpoints. Existing methods typically assume fixed input/output densities or rely on image-based representations, making them less suitable for real-world scenarios with variable sparsity and limited supervision. In this paper, we introduce Density-agnostic and Class-aware Network (DANCE), a novel framework that completes only the missing regions while preserving the observed geometry. DANCE generates candidate points via ray-based sampling from multiple viewpoints. A transformer decoder then refines their positions and predicts opacity scores, which determine the validity of each point for inclusion in the final surface. To incorporate semantic guidance, a lightweight classification head is trained directly on geometric features, enabling category-consistent completion without external image supervision. Extensive experiments on the PCN and MVP benchmarks show that DANCE outperforms state-of-the-art methods in accuracy and structural consistency, while remaining robust to varying input densities and noise levels.

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