Papers › PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud
PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud
Qiao Yu, Xianzhi Li, Yuan Tang, Xu Han, Jinfeng Xu, Long Hu, Min Chen
Reconstructing textured meshes from colored point clouds is an important but challenging task. Most existing methods yield blurry-looking textures or rely on 3D training data that are hard to acquire. Regarding this, we propose PointDreamer, a novel framework for textured mesh reconstruction from colored point cloud via diffusion-based 2D inpainting. Specifically, we first reconstruct an untextured mesh. Next, we project the input point cloud into 2D space to generate sparse multi-view images, and then inpaint empty pixels utilizing a pre-trained 2D diffusion model. After that, we unproject the colors of the inpainted dense images onto the untextured mesh, thus obtaining the final textured mesh. This project-inpaint-unproject pipeline bridges the gap between 3D point clouds and 2D diffusion models for the first time. Thanks to the powerful 2D diffusion model pre-trained on extensive 2D data, PointDreamer reconstructs clear, high-quality textures with high robustness to sparse or noisy input. Also, it's zero-shot requiring no extra training. In addition, we design Non-Border-First unprojection strategy to address the border-area inconsistency issue, which is less explored but commonly-occurred in methods that generate 3D textures from multiview images. Extensive qualitative and quantitative experiments on various synthetic and real-scanned datasets show the SoTA performance of PointDreamer, by significantly outperforming baseline methods with 30% improvement in LPIPS score (from 0.118 to 0.068). Code at: https://github.com/YuQiao0303/PointDreamer.
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