Robust 3D Shape Reconstruction in Zero-Shot from a Single Image in the Wild

• URL: http://arxiv.org/abs/2403.14539v2
• Date: Thu, 28 Nov 2024 13:53:55 GMT
• Title: Robust 3D Shape Reconstruction in Zero-Shot from a Single Image in the Wild
• Authors: Junhyeong Cho, Kim Youwang, Hunmin Yang, Tae-Hyun Oh,
• Abstract summary: We propose a unified regression model that integrates segmentation and reconstruction, specifically designed for 3D shape reconstruction.<n>We also introduce a scalable data synthesis pipeline that simulates a wide range of variations in objects, occluders, and backgrounds.<n>Our training on our synthetic data enables the proposed model to achieve state-of-the-art zero-shot results on real-world images.
• Score: 22.82439286651921
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent monocular 3D shape reconstruction methods have shown promising zero-shot results on object-segmented images without any occlusions. However, their effectiveness is significantly compromised in real-world conditions, due to imperfect object segmentation by off-the-shelf models and the prevalence of occlusions. To effectively address these issues, we propose a unified regression model that integrates segmentation and reconstruction, specifically designed for occlusion-aware 3D shape reconstruction. To facilitate its reconstruction in the wild, we also introduce a scalable data synthesis pipeline that simulates a wide range of variations in objects, occluders, and backgrounds. Training on our synthetic data enables the proposed model to achieve state-of-the-art zero-shot results on real-world images, using significantly fewer parameters than competing approaches.

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