PE3R: Perception-Efficient 3D Reconstruction

Understand

Recent advances in 2D-to-3D perception have enabled the recovery of 3D scene semantics from unposed images.

• However, prevailing methods often suffer from limited generalization, reliance on per-scene optimization, and semantic inconsistencies across viewpoints.
• To address these limitations, we introduce PE3R, a tuning-free framework for efficient and generalizable 3D semantic reconstruction.
• By integrating multi-view geometry with 2D semantic priors in a feed-forward pipeline, PE3R achieves zero-shot generalization across diverse scenes and object categories without any scene-specific fine-tuning.