Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve

• URL: http://arxiv.org/abs/2007.13034v1
• Date: Sun, 26 Jul 2020 00:08:37 GMT
• Title: Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve
• Authors: Weicheng Kuo, Anelia Angelova, Tsung-Yi Lin, Angela Dai
• Abstract summary: We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an image. We present Mask2CAD, which jointly detects objects in real-world images and for each detected object, optimize for the most similar CAD model and its pose. This produces a clean, lightweight representation of the objects in an image.
• Score: 54.054575408582565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object recognition has seen significant progress in the image domain, with focus primarily on 2D perception. We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an image by constructing a CAD-based representation of the objects and their poses. We present Mask2CAD, which jointly detects objects in real-world images and for each detected object, optimizes for the most similar CAD model and its pose. We construct a joint embedding space between the detected regions of an image corresponding to an object and 3D CAD models, enabling retrieval of CAD models for an input RGB image. This produces a clean, lightweight representation of the objects in an image; this CAD-based representation ensures a valid, efficient shape representation for applications such as content creation or interactive scenarios, and makes a step towards understanding the transformation of real-world imagery to a synthetic domain. Experiments on real-world images from Pix3D demonstrate the advantage of our approach in comparison to state of the art. To facilitate future research, we additionally propose a new image-to-3D baseline on ScanNet which features larger shape diversity, real-world occlusions, and challenging image views.

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