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Reconstructing vehicles from orthographic drawings using deep neural networks

arXiv 2206.08789 ↗First posted 14 Jun 2022cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

This paper explores the current state-of-the-art of object reconstruction from multiple orthographic drawings using deep neural networks. It proposes two algorithms to extract multiple views from a single image. The paper proposes a system based on pixel-aligned implicit functions (PIFu) and develops an advanced sampling strategy to generate signed distance samples. It also compares this approach to depth map regression from multiple views. Additionally, the paper uses a novel dataset for vehicle reconstruction from the racing game Assetto Corsa, which features higher quality models than the commonly used ShapeNET dataset. The trained neural network generalizes well to real-world inputs and creates plausible and detailed reconstructions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 32 references that are in the map. First posted on arXiv 14 Jun 2022.

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References · 25

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  5. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  6. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  7. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  8. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
  9. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  10. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  11. 2020PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitizationcited by 521
  12. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 390
13 more
  1. 2020ARCH: Animatable Reconstruction of Clothed Humanscited by 257
  2. 2017Learning a Multi-View Stereo Machinecited by 194
  3. 2019DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracingcited by 180
  4. 2017SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsiscited by 128
  5. 2019Learning to Infer Implicit Surfaces without 3D Supervisioncited by 103
  6. 2019Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Eracited by 87
  7. 2020Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Imagescited by 65
  8. 2020Adversarial Generation of Continuous Implicit Shape Representationscited by 46
  9. 20173D Shape Reconstruction from Sketches via Multi-view Convolutional Networkscited by 45
  10. 2017SurfNet: Generating 3D shape surfaces using deep residual networkscited by 32
  11. 2018Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapescited by 25
  12. 2018MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Imagecited by 17
  13. 2018Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representationcited by 7

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