PE3R: Perception-Efficient 3D Reconstruction
This addresses efficiency and accuracy challenges in 3D reconstruction for computer vision applications, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles the problem of limited generalization, suboptimal accuracy, and slow speed in 2D-to-3D reconstruction by proposing PE3R, which achieves a minimum 9-fold speedup and substantial gains in perception accuracy and reconstruction precision.
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.