PE3R: Perception-Efficient 3D Reconstruction
Authors: name: Jie Hu, name: Shizun Wang, name: Xinchao Wang

Recent advancements in 2D-to-3D perception have significantly improved the
understanding of 3D scenes from 2D images. However, existing methods face
critical challenges, including limited generalization across scenes, suboptimal
perception accuracy, and slow reconstruction speeds. To address these
limitations, we propose Perception-Efficient 3D Reconstruction (PE3R), a novel
framework designed to enhance both accuracy and efficiency. PE3R employs a
feed-forward architecture to enable rapid 3D semantic field reconstruction. The
framework demonstrates robust zero-shot generalization across diverse scenes
and objects while significantly improving reconstruction speed. Extensive
experiments on 2D-to-3D open-vocabulary segmentation and 3D reconstruction
validate the effectiveness and versatility of PE3R. The framework achieves a
minimum 9-fold speedup in 3D semantic field reconstruction, along with
substantial gains in perception accuracy and reconstruction precision, setting
new benchmarks in the field. The code is publicly available at:
https://github.com/hujiecpp/PE3R.