PE3R: Perception-Efficient 3D Reconstruction
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.
67cfa44c3a9d50150f59ffe1
Jie Hu
67cfa44c3a9d50150f59ffe2
Shizun Wang
admin_assigned
2025-03-11T14:40:56.536Z
63492af03caab0136df08d25
https://cdn-avatars.huggingface.co/v1/production/uploads/63492af03caab0136df08d25/-aZzajKl38jT29A-kziCI.jpeg
Shizun Wang
user
littlepure2333
67cfa44c3a9d50150f59ffe3
Xinchao Wang
admin_assigned
2025-03-11T14:40:50.238Z
63fc03a50aab060792ffef39
/avatars/9d5b1bb2a41928e08176b703935133ab.svg
Wangxinchao
user
wxcTest
PE3R: Perception-Efficient 3D Reconstruction
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.
A new framework, PE3R, enhances 2D-to-3D reconstruction with feed-forward architecture, achieving significant speed improvements and accuracy gains.
feed-forward architecture 3D semantic field reconstruction zero-shot generalization 2D-to-3D open-vocabulary segmentation