X-Ray: A Sequential 3D Representation For Generation
Tao Hu, Wenhang Ge, Yuyang Zhao, Gim Hee Lee
摘要
We introduce X-Ray, a novel 3D sequential representation inspired by the penetrability of x-ray scans. X-Ray transforms a 3D object into a series of surface frames at different layers, making it suitable for generating 3D models from images. Our method utilizes ray casting from the camera center to capture geometric and textured details, including depth, normal, and color, across all intersected surfaces. This process efficiently condenses the whole 3D object into a multi-frame video format, motivating the utilize of a network architecture similar to those in video diffusion models. This design ensures an efficient 3D representation by focusing solely on surface information. Also, we propose a two-stage pipeline to generate 3D objects from X-Ray Diffusion Model and Upsampler. We demonstrate the practicality and adaptability of our X-Ray representation by synthesizing the complete visible and hidden surfaces of a 3D object from a single input image. Experimental results reveal the state-of-the-art superiority of our representation in enhancing the accuracy of 3D generation, paving the way for new 3D representation research and practical applications.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper4
- PRM: Photometric Stereo Based Large Reconstruction ModelWenhang Ge, Jiantao Lin, Guibao Shen, Jiawei Feng 等ICCV 2025 · 被引用 1 次
- LaRI: Layered Ray Intersections for Single-view 3D Geometric ReasoningRui Li, Biao Zhang, Zhenyu Li, Federico Tombari 等ICML 2026
- ORCaS: Unsupervised Depth Completion via Occluded Region Completion as SupervisionHyoungseob Park, Runjian Chen, Patrick Rim, Dong Lao 等ICLR 2026
- MAR-3D: Progressive Masked Auto-regressor for High-Resolution 3D GenerationJinnan Chen, Lingting Zhu, Zeyu Hu, Shengju Qian 等CVPR 2025
它引用的顶会 Paper29
- An Image is Worth 16x16 Words: Transformers for Image Recognition at ScaleAlexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn 等ICLR 2021 · 被引用 21,477 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li 等NeurIPS 2022 · 被引用 8,965 次
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- Scalable Diffusion Models with TransformersWilliam Peebles, Saining XieICCV 2023 · 被引用 5,568 次
相关 Paper
- Structure-Aware Sparse-View X-Ray 3D ReconstructionYuanhao Cai, Jiahao Wang, Alan L. Yuille, Zongwei Zhou 等CVPR 2024
- Denoising Diffusion via Image-Based RenderingTitas Anciukevicius, Fabian Manhardt, Federico Tombari, Paul HendersonICLR 2024 · 被引用 19 次
- Revisiting CAD Model Generation by Learning Raster SketchPu Li, Wenhao Zhang, Jianwei Guo, Jinglu Chen 等AAAI 2025 · 被引用 7 次
- RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and GenerationTitas Anciukevicius, Zexiang Xu, Matthew Fisher, Paul Henderson 等CVPR 2023
- Coherent 3D Scene Diffusion From a Single RGB ImageManuel Dahnert, Angela Dai, Norman Müller, Matthias NießnerNeurIPS 2024 · 被引用 10 次
