mlsanityData to 7 Oct 2026

Survey on Fundamental Deep Learning 3D Reconstruction Techniques

arXiv 2407.08137 ↗First posted 11 Jul 2024cs.CV cs.GRcited by 2

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

Abstract (arXiv)

This survey aims to investigate fundamental deep learning (DL) based 3D reconstruction techniques that produce photo-realistic 3D models and scenes, highlighting Neural Radiance Fields (NeRFs), Latent Diffusion Models (LDM), and 3D Gaussian Splatting. We dissect the underlying algorithms, evaluate their strengths and tradeoffs, and project future research trajectories in this rapidly evolving field. We provide a comprehensive overview of the fundamental in DL-driven 3D scene reconstruction, offering insights into their potential applications and limitations.

Key facts. Cited by 2 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 10 of its 16 references that are in the map. First posted on arXiv 11 Jul 2024.

Builds on

10 of 16 references.

References · 10

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  4. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  5. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  6. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  7. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,144
  8. 2024LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creationcited by 730
  9. 2024InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Modelscited by 421
  10. 2022Differentiable Point-Based Radiance Fields for Efficient View Synthesiscited by 86

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025ViT-NeBLa: A Hybrid Vision Transformer and Neural Beer-Lambert Framework for Single-View 3D Reconstruction of Oral Anatomy from Panoramic Radiographscited by 2
  2. 2025Deep Learning Perspective of Scene Understanding in Autonomous Robotscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025DT-NeRF: A Diffusion and Transformer-Based Optimization Approach for Neural Radiance Fields in 3D Reconstruction
  2. 2023ReconFusion: 3D Reconstruction with Diffusion Priors
  3. 2025Diffusion-Guided Gaussian Splatting for Large-Scale Unconstrained 3D Reconstruction and Novel View Synthesis
  4. 2024Wonderland: Navigating 3D Scenes from a Single Image
  5. 2026Deep Learning Approaches for 3D Medical Scene Completion: From Geometric Modeling to Generative Paradigms
  6. 2025Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models
  7. 2025Beyond Existance: Fulfill 3D Reconstructed Scenes with Pseudo Details
  8. 2024Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstruction