title: PE3R: Perception-Efficient 3D Reconstruction
summary: 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.
paper_authors._id: 67cfa44c3a9d50150f59ffe1
paper_authors.name: Jie Hu
paper_authors._id: 67cfa44c3a9d50150f59ffe2
paper_authors.name: Shizun Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:40:56.536Z
paper_authors._id: 63492af03caab0136df08d25
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63492af03caab0136df08d25/-aZzajKl38jT29A-kziCI.jpeg
paper_authors.fullname: Shizun Wang
paper_authors.type: user
paper_authors.user: littlepure2333
paper_authors._id: 67cfa44c3a9d50150f59ffe3
paper_authors.name: Xinchao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:40:50.238Z
paper_authors._id: 63fc03a50aab060792ffef39
paper_authors.avatarUrl: /avatars/9d5b1bb2a41928e08176b703935133ab.svg
paper_authors.fullname: Wangxinchao
paper_authors.type: user
paper_authors.user: wxcTest
paper_title: PE3R: Perception-Efficient 3D Reconstruction
paper_summary: 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.
paper_ai_keywords: feed-forward architecture 3D semantic field reconstruction zero-shot generalization 2D-to-3D open-vocabulary segmentation perception accuracy reconstruction precision speedup