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2nd Place Solution for IJCAI-PRICAI 2020 3D AI Challenge: 3D Object Reconstruction from A Single Image

arXiv 2105.13575 ↗First posted 28 May 2021cs.CV cs.AIcited by 0

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Abstract (arXiv)

In this paper, we present our solution for the it IJCAI--PRICAI--20 3D AI Challenge: 3D Object Reconstruction from A Single Image. We develop a variant of AtlasNet that consumes single 2D images and generates 3D point clouds through 2D to 3D mapping. To push the performance to the limit and present guidance on crucial implementation choices, we conduct extensive experiments to analyze the influence of decoder design and different settings on the normalization, projection, and sampling methods. Our method achieves 2nd place in the final track with a score of 70.88, a chamfer distance of 36.87, and a mean f-score of 59.18. The source code of our method will be available at https://github.com/em-data/EnhancedAtlasNet3DReconstruction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 12 references that are in the map. First posted on arXiv 28 May 2021.

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9 of 12 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  4. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  5. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
  6. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 588
  7. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  8. 20203D-FUTURE: 3D Furniture shape with TextUREcited by 311
  9. 2019BSP-Net: Generating Compact Meshes via Binary Space Partitioningcited by 206

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