mlsanityData to 7 Oct 2026

X-Ray: A Sequential 3D Representation For Generation

arXiv 2404.14329 ↗First posted 22 Apr 2024cs.CVcited by 9

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

We introduce X-Ray, a novel 3D sequential representation inspired by the penetrability of x-ray scans. X-Ray transforms a 3D object into a series of surface frames at different layers, making it suitable for generating 3D models from images. Our method utilizes ray casting from the camera center to capture geometric and textured details, including depth, normal, and color, across all intersected surfaces. This process efficiently condenses the whole 3D object into a multi-frame video format, motivating the utilize of a network architecture similar to those in video diffusion models. This design ensures an efficient 3D representation by focusing solely on surface information. Also, we propose a two-stage pipeline to generate 3D objects from X-Ray Diffusion Model and Upsampler. We demonstrate the practicality and adaptability of our X-Ray representation by synthesizing the complete visible and hidden surfaces of a 3D object from a single input image. Experimental results reveal the state-of-the-art superiority of our representation in enhancing the accuracy of 3D generation, paving the way for new 3D representation research and practical applications.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 56 of its 67 references that are in the map. First posted on arXiv 22 Apr 2024.

Builds on

56 of 67 references.

Closest ancestors · 9

Same direction, newest first

  1. 2024SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion18 Mar
  2. 2023Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation12 Dec
  3. 2023SyncDreamer: Generating Multiview-consistent Images from a Single-view Image7 Sep
  4. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization29 Jun
  5. 2022Point-E: A System for Generating 3D Point Clouds from Complex Prompts16 Dec
  6. 2022Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation1 Dec
  7. 2022LION: Latent Point Diffusion Models for 3D Shape Generation12 Oct
  8. 2022DreamFusion: Text-to-3D using 2D Diffusion29 Sep
  9. 2021Diffusion Probabilistic Models for 3D Point Cloud Generation2 Mar

Other references · 47

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  5. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  6. 2022Scalable Diffusion Models with Transformerscited by 5,931
  7. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  8. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  9. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  10. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
  11. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  12. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
35 more
  1. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  2. 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
  3. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
  4. 2020Neural Sparse Voxel Fieldscited by 959
  5. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
  6. 2023DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creationcited by 837
  7. 2023LRM: Large Reconstruction Model for Single Image to 3Dcited by 834
  8. 2024LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creationcited by 730
  9. 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
  10. 2022Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Itemscited by 601
  11. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  12. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  13. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 487
  14. 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
  15. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  16. 2021Text2Mesh: Text-Driven Neural Stylization for Meshescited by 332
  17. 2023VideoFusion: Decomposed Diffusion Models for High-Quality Video Generationcited by 330
  18. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 324
  19. 2020Single-View View Synthesis with Multiplane Imagescited by 263
  20. 2023Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformerscited by 257
  21. 2024TripoSR: Fast 3D Object Reconstruction from a Single Imagecited by 227
  22. 2023DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Modelcited by 213
  23. 2018PU-Net: Point Cloud Upsampling Networkcited by 208
  24. 2023MeshDiffusion: Score-based Generative 3D Mesh Modelingcited by 198
  25. 2022EfficientNeRF: Efficient Neural Radiance Fieldscited by 110
  26. 2021Point Cloud Upsampling via Disentangled Refinementcited by 74
  27. 2020Generative Sparse Detection Networks for 3D Single-shot Object Detectioncited by 69
  28. 2023Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflectioncited by 68
  29. 2024Advances in 3D Generation: A Surveycited by 62
  30. 2023Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Modelscited by 50
  31. 2023ZeroShape: Regression-based Zero-shot Shape Reconstructioncited by 30
  32. 2024Make-A-Shape: a Ten-Million-scale 3D Shape Modelcited by 25
  33. 2023Ghost on the Shell: An Expressive Representation of General 3D Shapescited by 24
  34. 2023TriVol: Point Cloud Rendering via Triple Volumescited by 16
  35. 2023Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fieldscited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 1.

9 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior
  2. 20243DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
  3. 2024Direct and Explicit 3D Generation from a Single Image
  4. 2023One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion
  5. 2024Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors
  6. 2024CAT3D: Create Anything in 3D with Multi-View Diffusion Models
  7. 2026Diffusion-Based Material Regularization for Physics-Based Inverse Rendering
  8. 2024Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models