FastCAD: Real-Time CAD Retrieval and Alignment from Scans and Videos
This work addresses the need for efficient, precise 3D digitization in augmented reality and robotics, offering a significant performance improvement over prior methods.
The paper tackles the problem of real-time 3D CAD model retrieval and alignment from scans and videos, achieving a 50x speedup in inference time and improving alignment accuracy from 43.0% to 48.2% and reconstruction accuracy from 22.9% to 29.6% in video settings.
Digitising the 3D world into a clean, CAD model-based representation has important applications for augmented reality and robotics. Current state-of-the-art methods are computationally intensive as they individually encode each detected object and optimise CAD alignments in a second stage. In this work, we propose FastCAD, a real-time method that simultaneously retrieves and aligns CAD models for all objects in a given scene. In contrast to previous works, we directly predict alignment parameters and shape embeddings. We achieve high-quality shape retrievals by learning CAD embeddings in a contrastive learning framework and distilling those into FastCAD. Our single-stage method accelerates the inference time by a factor of 50 compared to other methods operating on RGB-D scans while outperforming them on the challenging Scan2CAD alignment benchmark. Further, our approach collaborates seamlessly with online 3D reconstruction techniques. This enables the real-time generation of precise CAD model-based reconstructions from videos at 10 FPS. Doing so, we significantly improve the Scan2CAD alignment accuracy in the video setting from 43.0% to 48.2% and the reconstruction accuracy from 22.9% to 29.6%.