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.

使用创新的视图合成和三维重建技术，本研究旨在利用单 ocular RGB 图像创建濒危物种模型。使用 SyncDreamer 生成独特视角，利用 NeuS 和 NeRF 重建三维表示。选择东方白鹳、青蛙、蜻蜓和老虎四种不同动物作为研究对象，结果表明，SyncDreamer、NeRF 和 NeuS 技术的组合能成功创建濒危动物的三维模型。然而，我们还观察到 NeuS 生成的图像模糊，而 NeRF 生成的图像更清晰但带有噪点。该研究突显了对濒危动物建模的潜力，并为未来研究提供了新的方向。通过展示这些先进技术的有效性，我们希望鼓励进一步探索和发展用于保护和研究濒危物种的技术。

使用 NeRF 和 NeuS 进行濒危动物物种的三维重建的 SyncDreamer

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

In this paper, we present a novel diffusion model called that generates
multiview-consistent images from a single-view image. Using pretrained
large-scale 2D diffusion models, recent work Zero123 demonstrates the ability
to generate plausible novel views from a single-view image of an object.
However, maintaining consistency in geometry and colors for the generated
images remains a challenge. To address this issue, we propose a synchronized
multiview diffusion model that models the joint probability distribution of
multiview images, enabling the generation of multiview-consistent images in a
single reverse process. SyncDreamer synchronizes the intermediate states of all
the generated images at every step of the reverse process through a 3D-aware
feature attention mechanism that correlates the corresponding features across
different views. Experiments show that SyncDreamer generates images with high
consistency across different views, thus making it well-suited for various 3D
generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.

我们提出了一个同步多视角扩散模型 SyncDreamer，该模型可以从单视图图像生成多视角一致的图像，解决了几何和颜色保持一致性的挑战。实验显示，SyncDreamer 生成的图像在不同视角上具有高度一致性，因此非常适合于新视点综合、文本到三维和图像到三维等各种三维生成任务。