The paper introduces a self‑supervised, three‑stage pose estimation model for construction machines, leveraging contrastive learning (SimCLR) in the pre‑train stage and two fine‑tuning stages for transfer learning and downstream tasks. It trains on a large unlabeled ACID dataset and fine‑tunes on smaller simulated datasets, achieving 11% and 13% accuracy improvements over baseline self‑supervised models using ResNet‑50 and HRNet‑W32 backbones, respectively.
arXiv:2606. 12988v1 Announce Type: cross Abstract: This paper introduces a new methodology for real-time prediction of ergonomic and non-ergonomic human poses using volumetric video data in three dimensions.
arXiv:2606. 05445v1 Announce Type: new Abstract: We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks.
arXiv:2609.38755v1 Announce Type: new Abstract: A wide range of approaches have been developed for camera pose estimation, including correspondence-based methods, end-to-end pose regression, and rece...
arXiv:2606. 16042v1 Announce Type: cross Abstract: This work explores the use of artificial intelligence in mobile robotics to achieve autonomous detection and pose estimation of load carriers for automated pickup.
arXiv:2606.07419v4 Announce Type: replace Abstract: Recovering 3D human poses for multiple individuals from different camera views is a fundamental bottleneck for analyzing interacting behaviors. Exi...
arXiv:2607. 04930v1 Announce Type: cross Abstract: In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effective representations from data, yet they primarily encode category-level patterns into fixed shape priors or static parameter weights, which limits their scalability to highly diverse instances.
The paper introduces G2G, a method that leverages known intra-group geometry to estimate the relative 6-DoF pose between two image groups, a key problem in cross-sequence relocalization and multi-camera rig odometry. G2G keeps a frozen foundation model and adds three lightweight trainable modules—a perceiver resampler, a cross-group bridge with merged self-attention, and a multi-frame pose head—totaling about 32 M parameters, less than 6% of the full model. Evaluated on four diverse datasets covering indoor/outdoor simulation, real-world cross-season capture, and zero-shot sim-to-real transfer, G2G achieves state‑of‑the‑art accuracy on both pose estimation tasks while only requiring supervision from relative poses.
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.
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.
arXiv:2202. 14019v3 Announce Type: replace-cross Abstract: Maintaining proper form while exercising is important for preventing injuries and maximizing muscle mass gains.
arXiv:2608. 19968v1 Announce Type: cross Abstract: Modern computer vision has enabled partial autonomy in robotic assembly manipulation.
In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effective representations from data, yet they primarily encode category-level patterns into fixed shape priors or static parameter weights, which limits their scalability to highly diverse instances. In this paper, we rethink category-level pose estimation from a memory-centric perspective and present MemPose, a memory-augmented framework that explicitly incorporates category-level geometric memory into the pose estimation pipeline.