In this paper, we present a novel shape reconstruction method leveraging
diffusion model to generate 3D sparse point cloud for the object captured in a
single RGB image. Recent methods typically leverage global embedding or local
projection-based features as the condition to guide the diffusion model.
However, such strategies fail to consistently align the denoised point cloud
with the given image, leading to unstable conditioning and inferior
performance. In this paper, we present CCD-3DR, which exploits a novel centered
diffusion probabilistic model for consistent local feature conditioning. We
constrain the noise and sampled point cloud from the diffusion model into a
subspace where the point cloud center remains unchanged during the forward
diffusion process and reverse process. The stable point cloud center further
serves as an anchor to align each point with its corresponding local
projection-based features. Extensive experiments on synthetic benchmark
ShapeNet-R2N2 demonstrate that CCD-3DR outperforms all competitors by a large
margin, with over 40% improvement. We also provide results on real-world
dataset Pix3D to thoroughly demonstrate the potential of CCD-3DR in real-world
applications. Codes will be released soon

该论文提出了一种利用扩散模型生成单个 RGB 图像中捕捉到的物体的三维稀疏点云的新型形状重构方法，该方法利用了一种新的中心扩散概率模型，用于一致的局部特征条件约束。在合成基准 ShapeNet-R2N2 和实际数据集 Pix3D 上进行的大量实验表明，CCD-3DR 在性能上优于竞争对手超过 40％。