Abstract
Predictive video encoders provide dense visual representations, but adapting them to 3D reconstruction requires accurate depth, camera poses, and cross-view geometry. We study supervised RGB-only multi-view reconstruction using an architecture that combines video-pretrained features, camera estimation, scene aggregation, epipolar transport, and residual pointmaps. Experiments compare global and spatial camera regression with joint relative objectives on CO3D, alongside an independently trained TUM RGB-D model. On 168 CO3D five-view windows, the spatial-camera configuration achieves 29.3% lower absolute trajectory error than the global-camera configuration, but performs worse than identity poses in trajectory error and worse than the global configuration in depth error. The joint relative-objective configuration improves normalized intrinsics error and similarity-aligned Chamfer distance from 0.6447 to 0.5573, but increases raw Chamfer distance from 3.5535 to 4.4374. On 163 indoor windows, camera error is 0.3063 m versus 0.3694 m for identity poses, while depth error is 0.4279 versus 0.2516 for constant median depth. Refinement worsens indoor camera error, and additional experiments show competing depth and pose effects. Overall, the results distinguish representation adaptation, aligned shape, and raw reconstruction, highlighting the need for isolated component and encoder comparisons before attributing reconstruction gains to predictive video pretraining.
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URL
https://arxiv.org/abs/2611.15143
