# Fudan's F2G-Pose predicts 6D pose, metric size and full shape from one RGB-D frame at 28 frames per second

The NeurIPS 2026 method lifts foundation-model features onto point clouds and routes them through an eight-expert transformer, running about seven times faster than diffusion-based GenPose++.

- Published: 2026-10-24T06:20:43.691Z
- Canonical: https://polylog.news/ai/2026-10-24/fudan-s-f2g-pose-predicts-6d-pose-metric-size-and-full-shape
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [Polylog editors](https://polylog.news), [arXiv](https://arxiv.org/abs/2610.31623), [AI Weekly](https://aiweekly.co/alerts/fudans-f2g-pose-jointly-predicts-6d-pose-size-shape-at-28-fps)

F2G-Pose, from researchers at Fudan University, is a category-level object pose estimator. From a single segmented RGB-D observation it predicts 6D pose, metric size and a completed camera-frame shape in one feed-forward pass. The code is r…

This story is for subscribers. Read it in full at https://polylog.news/ai/2026-10-24/fudan-s-f2g-pose-predicts-6d-pose-metric-size-and-full-shape (subscription information: https://polylog.news/pricing).