The Baseline Methods of Wonder3D and What They Mean | HackerNoon

Table of Links

Abstract and 1 Introduction

2. Related Works

2.1. 2D Diffusion Models for 3D Generation

2.2. 3D Generative Models and 2.3. Multi-view Diffusion Models

3. Problem Formulation

3.1. Diffusion Models

3.2. The Distribution of 3D Assets

4. Method and 4.1. Consistent Multi-view Generation

4.2. Cross-Domain Diffusion

4.3. Textured Mesh Extraction

5. Experiments

5.1. Implementation Details

5.2. Baselines

5.3. Evaluation Protocol

5.4. Single View Reconstruction

5.5. Novel View Synthesis and 5.6. Discussions

6. Conclusions and Future Works, Acknowledgements and References

5.2. Baselines

We adopt Zero123 [31], RealFusion [38], Magic123 [44], One-2-3-45 [30], Point-E [41], Shap-E [25] and a recent work SyncDreamer [33] as baseline methods. Given an input image, zero123 is capable of generating novel views of arbitrary viewpoints, and it can be incorporated with SDS loss [43] for 3D reconstruction (we adopt the implementation of ThreeStudio [20]). RealFusion [38] and Magic123 [44] leverage Stable Diffusion [47] and SDS loss for single-view reconstruction. One-2-3-45 [30] directly predict SDFs via SparseNeuS [36] by taking the generated multiple images of Zero123 [31]. Point-E [41] and ShapE [25] are 3D generative models trained on a large internal OpenAI 3D dataset, both of which are able to convert a single-view image into a point cloud or an implicit representation. SyncDreamer[33] aims to generate multi-view consistent images from a single image for deriving 3D geometry.