Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction
원문 초록
We introduce Geo4D, a method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic prior captured by such video models, Geo4D can be trained using only synthetic data while generalizing well to real data in a zero-shot manner. Geo4D predicts several complementary geometric modalities, namely point, depth, and ray maps. It uses a new multi-modal alignment algorithm to align and fuse these modalities, as well as multiple sliding windows, at inference time, thus obtaining robust and accurate 4D reconstruction of long videos. Extensive experiments across multiple benchmarks show that Geo4D significantly surpasses state-of-the-art video depth estimation methods, including recent methods such as MonST3R, which are also designed to handle dynamic scenes.
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Test3R: Learning to Reconstruct 3D at Test Time
POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction
Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
BulletGen: Improving 4D Reconstruction with Bullet-Time Generation
4Real-Video-V2: Fused View-Time Attention and Feedforward Reconstruction for 4D Scene Generation
Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation
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