mlsanityData to 7 Oct 2026

SyncDreamer for 3D Reconstruction of Endangered Animal Species with NeRF and NeuS

arXiv 2312.13832 ↗First posted 21 Dec 2023cs.CVcited by 2

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Abstract (arXiv)

The main aim of this study is to demonstrate how innovative view synthesis and 3D reconstruction techniques can be used to create models of endangered species using monocular RGB images. To achieve this, we employed SyncDreamer to produce unique perspectives and NeuS and NeRF to reconstruct 3D representations. We chose four different animals, including the oriental stork, frog, dragonfly, and tiger, as our subjects for this study. Our results show that the combination of SyncDreamer, NeRF, and NeuS techniques can successfully create 3D models of endangered animals. However, we also observed that NeuS produced blurry images, while NeRF generated sharper but noisier images. This study highlights the potential of modeling endangered animals and offers a new direction for future research in this field. By showcasing the effectiveness of these advanced techniques, we hope to encourage further exploration and development of techniques for preserving and studying endangered species.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 26 references that are in the map. First posted on arXiv 21 Dec 2023.

Builds on

23 of 26 references.

References · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  5. 2022Generative Adversarial Networkscited by 5,603
  6. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  7. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  8. 2021Volume Rendering of Neural Implicit Surfacescited by 990
  9. 2023MVDream: Multi-view Diffusion for 3D Generationcited by 928
  10. 2023DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creationcited by 837
  11. 2023Wonder3D: Single Image to 3D using Cross-Domain Diffusioncited by 682
  12. 2023SyncDreamer: Generating Multiview-consistent Images from a Single-view Imagecited by 638
11 more
  1. 2022MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstructioncited by 467
  2. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
  3. 2021How Much Can CLIP Benefit Vision-and-Language Tasks?cited by 321
  4. 2022Novel View Synthesis with Diffusion Modelscited by 299
  5. 2023Generative Novel View Synthesis with 3D-Aware Diffusion Modelscited by 273
  6. 2022SparseFusion: Distilling View-conditioned Diffusion for 3D Reconstructioncited by 216
  7. 2023NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusioncited by 179
  8. 2022RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generationcited by 162
  9. 2022NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priorscited by 156
  10. 2023Consistent View Synthesis with Pose-Guided Diffusion Modelscited by 117
  11. 2023Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Datacited by 113

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Citing papers per year: 2024: 2.

2 papers.

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