MoReMouse: Monocular Reconstruction of Laboratory Mouse
Yuan Zhong, Jingxiang Sun, Zhongbin Zhang, Liang An, Yebin Liu
摘要
Laboratory mice, particularly the C57BL/6 strain, are essential animal models in biomedical research. However, accurate 3D surface motion reconstruction of mice remains a significant challenge due to their complex non-rigid deformations, textureless fur-covered surfaces, and the lack of realistic 3D mesh models. Moreover, existing visual datasets for mice reconstruction only contain sparse viewpoints without 3D geometries. To fill the gap, we introduce MoReMouse, the first monocular dense 3D reconstruction network specifically designed for C57BL/6 mice. To achieve high-fidelity 3D reconstructions, we present three key innovations. First, we create the first high-fidelity, dense-view synthetic dataset for C57BL/6 mice by rendering a realistic, anatomically accurate Gaussian mouse avatar. Second, MoReMouse leverages a transformer-based feedforward architecture combined with triplane representation, enabling high-quality 3D surface generation from a single image, optimized for the intricacies of small animal morphology. Third, we propose geodesic-based continuous correspondence embeddings on the mouse surface, which serve as strong semantic priors, improving surface consistency and reconstruction stability, especially in highly dynamic regions like limbs and tail. Through extensive quantitative and qualitative evaluations, we demonstrate that MoReMouse significantly outperforms existing open-source methods in both accuracy and robustness.
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。
它引用的顶会 Paper18
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- Efficient Geometry-aware 3D Generative Adversarial NetworksEric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano 等CVPR 2022 · 被引用 984 次
- LRM: Large Reconstruction Model for Single Image to 3DYicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi 等ICLR 2024 · 被引用 813 次
- Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape SynthesisTianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu 等NeurIPS 2021 · 被引用 652 次
相关 Paper
- Motion 3-to-4: 3D Motion Reconstruction for 4D SynthesisHongyuan Chen, Xingyu Chen, Zexiang Xu, Anpei ChenCVPR 2026 · 被引用 17 次
- HumanRAM: Feed-forward Human Reconstruction and Animation Model using TransformersZhiyuan Yu, Zhe Li, Hujun Bao, Can Yang 等SIGGRAPH 2025 · 被引用 2 次
- ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape ReconstructionGengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic 等NeurIPS 2021 · 被引用 103 次
- MoRE: 3D Visual Geometry Reconstruction Meets Mixture-of-ExpertsJingnan Gao, Zhe Wang, Xianze Fang, Xingyu Ren 等CVPR 2026 · 被引用 19 次
- High-Fidelity Human Avatars from a Single RGB CameraHao Zhao, Jinsong Zhang, Yu-Kun Lai, Zerong Zheng 等CVPR 2022 · 被引用 38 次
