PnP-Transformer: A Neural Pose Solver for Zero-Shot 6D Object Pose Estimation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2609.24482v1 Announce Type: new Abstract: Monocular 3D human pose estimation (HPE) remains challenging due to depth ambiguity, occlu- sions, and the need for temporal consistency. While multi-v...
arXiv:2610.23039v1 Announce Type: new Abstract: Feed-forward Gaussian models have made novel view synthesis almost instantaneous. Feed-forward methods predict Gaussians directly, either in two stages...
The paper introduces a benchmark for evaluating Structure-from-Motion (SfM) methods used in novel view synthesis (NVS) such as NeRFs and 3DGS. It compares classical SfM, which relies on local feature matches and bundle adjustment, with recent neural network approaches that regress camera parameters directly for faster but less accurate results. The study finds that reducing feature counts speeds up classical SfM without sacrificing pose accuracy, and that combining neural network initial estimates with classical refinement yields the best balance of efficiency and effectiveness.
RYOPO is an end‑to‑end query‑based RGB‑D set predictor that jointly detects, segments, and estimates 9‑DoF poses of unseen instances within known categories without relying on external instance segmentation or CAD priors. It uses shared image and scene encoding, a query‑conditioned geometry pathway, and object‑centric refinement with pose‑conditioned cross‑attention to achieve accurate pose estimation. On benchmark datasets such as NOCS, REAL275, and HouseCat6D, RYOPO outperforms published methods and runs in real time at 31.8 FPS on an RTX A6000.
WAPR is a zero‑shot wide‑angle pose refinement model that can correct candidate 6D poses with rotational errors up to 90°, achieving fast inference (≤1 s per frame) and high throughput (≈25 detections per second). It leverages rotational symmetry priors to canonicalize pose targets and introduces the SA6D dataset, which augments 944 GSO scans into ~50 k object instances and ~2 M RGB‑D images. Experiments on seven BOP core datasets demonstrate that WAPR sets new state‑of‑the‑art performance for unseen‑object pose estimation in both fast and unconstrained settings.
arXiv:2609.22716v1 Announce Type: new Abstract: Image-to-LiDAR registration estimates the camera pose of an image with respect to a LiDAR point cloud. It has diverse applications in autonomous drivin...