CAOA - Completion-Assisted Object-CAD Alignment

Hiranya Garbha Kumar, Minhas Kamal, Balakrishnan Prabhakaran

Keywords: indoor semantic reconstruction, object-CAD alignment, point cloud completion, 9-DoF pose estimation

TL;DR: This paper presents CAOA, a context-aware point cloud completion and symmetry-aware object-CAD alignment framework with new synthetic and real-world datasets, achieving 17% better alignment than current SOTA on Scan2CAD.

Abstract:

Accurately aligning CAD models to their corresponding objects in indoor RGB-D scans is a central challenge in 3D semantic reconstruction. The task requires estimating a 9-Degree-of-Freedom (DoF) pose—position, rotation, and scale along three axes—but is hindered by noisy and incomplete scans, as well as segmentation errors that cause geometric distortions.
We present Completion-Assisted Object–CAD Alignment (CAOA), a method that integrates a semantically and contextually aware point cloud completion module with a symmetry-aware relative pose estimation algorithm, enabling precise alignment of CAD models to scanned objects.
Existing completion methods are typically trained and evaluated on synthetic datasets, which often fail to generalize to real-world scans. To bridge this gap, we introduce a synthetic data generation strategy tailored to indoor scenes, significantly reducing the synthetic-to-real domain gap—validated through quantitative comparisons with widely used completion datasets. In addition, we release S2C-Completion, an expert-annotated dataset of over 8,500 object–CAD pairs from Scan2CAD, created for real-world indoor single-object completion and intended as a new benchmark for this task.
For object–CAD alignment, we incorporate symmetry information via a symmetry-aware loss, improving robustness to symmetric ambiguities. On the Scan2CAD benchmark, CAOA achieves a 17\% accuracy improvement over state-of-the-art methods.
All code, datasets, and annotation tools will be publicly available on GitHub - https://github.com/kumarhiranya/S2CCompletion.

Submission Number: 162