{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pointdreamer-zero-shot-3d-textured-mesh","title":"PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud","arxiv_id":"2406.15811","date":"2024-06-22","proceeding":null,"authors":["Qiao Yu","Xianzhi Li","Yuan Tang","Xu Han","Jinfeng Xu","Long Hu","Min Chen"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2406.15811v2","url_pdf":"https://arxiv.org/pdf/2406.15811v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pointdreamer-zero-shot-3d-textured-mesh","repo_url":"https://github.com/yuqiao0303/pointdreamer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-inpainting","task_name":"Image Inpainting"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}