Matrix3D: Large Photogrammetry Model All-in-One
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Abstract (arXiv)
We present Matrix3D, a unified model that performs several photogrammetry subtasks, including pose estimation, depth prediction, and novel view synthesis using just the same model. Matrix3D utilizes a multi-modal diffusion transformer (DiT) to integrate transformations across several modalities, such as images, camera parameters, and depth maps. The key to Matrix3D's large-scale multi-modal training lies in the incorporation of a mask learning strategy. This enables full-modality model training even with partially complete data, such as bi-modality data of image-pose and image-depth pairs, thus significantly increases the pool of available training data. Matrix3D demonstrates state-of-the-art performance in pose estimation and novel view synthesis tasks. Additionally, it offers fine-grained control through multi-round interactions, making it an innovative tool for 3D content creation. Project page: https://nju-3dv.github.io/projects/matrix3d.
Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). Builds on 112 of its 148 references that are in the map. First posted on arXiv 11 Feb 2025.
Builds on
112 of 148 references.
References · 112
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50 most cited of 112 All 112 (JSON)
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