In this paper, we present a generalizable model-free 6-DoF object pose
estimator called Gen6D. Existing generalizable pose estimators either need
high-quality object models or require additional depth maps or object masks in
test time, which significantly limits their application scope. In contrast, our
pose estimator only requires some posed images of the unseen object and is able
to accurately predict the poses of the object in arbitrary environments. Gen6D
consists of an object detector, a viewpoint selector and a pose refiner, all of
which do not require the 3D object model and can generalize to unseen objects.
Experiments show that Gen6D achieves state-of-the-art results on two model-free
datasets: the MOPED dataset and a new GenMOP dataset collected by us. In
addition, on the LINEMOD dataset, Gen6D achieves competitive results compared
with instance-specific pose estimators. Project page:
this https URL

本文介绍了一种通用的无模型 6 自由度物体姿势估计模型 ——Gen6D。该模型不需要高质量物体模型，不需要深度图或物体遮蔽物，在任何环境下只需要物体的几张照片便可以准确预测物体的姿态。在实验中，Gen6D 在两个无模型数据集上达到了最新成果，以及在 LINEMOD 数据集上与特定实例的姿势估计相比展现出一定的竞争力。

Gen6D: 从 RGB 图像中实现通用的无模型 6-DoF 物体姿态估计

Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB  Images

Prior work on 6-DoF object pose estimation has largely focused on
instance-level processing, in which a textured CAD model is available for each
object being detected. Category-level 6-DoF pose estimation represents an
important step toward developing robotic vision systems that operate in
unstructured, real-world scenarios. In this work, we propose a single-stage,
keypoint-based approach for category-level object pose estimation that operates
on unknown object instances within a known category using a single RGB image as
input. The proposed network performs 2D object detection, detects 2D keypoints,
estimates 6-DoF pose, and regresses relative bounding cuboid dimensions. These
quantities are estimated in a sequential fashion, leveraging the recent idea of
convGRU for propagating information from easier tasks to those that are more
difficult. We favor simplicity in our design choices: generic cuboid vertex
coordinates, single-stage network, and monocular RGB input. We conduct
extensive experiments on the challenging Objectron benchmark, outperforming
state-of-the-art methods on the 3D IoU metric (27.6% higher than the MobilePose
single-stage approach and 7.1% higher than the related two-stage approach).

该研究提出了一种基于关键点的方法，通过一个 RGB 图像作为输入，对已知类别内的未知目标实例进行目录级别的物体位姿估计，采用单阶段神经网络，并在 Objectron 基准测试中表现出比现有方法更好的性能

基于关键点的单阶段类别级别物体姿态估计

Single-Stage Keypoint-Based Category-Level Object Pose Estimation from  an RGB Image

Standard 3D reconstruction pipelines assume stationary world, therefore
suffer from `ghost artifacts' whenever dynamic objects are present in the
scene. Recent approaches has started tackling this issue, however, they
typically either only discard dynamic information, represent it using bounding
boxes or per-frame depth or rely on approaches that are inherently slow and not
suitable to online settings.
We propose an end-to-end system for live reconstruction of large-scale
outdoor dynamic environments. We leverage recent advances in computationally
efficient data-driven approaches for 6-DoF object pose estimation to segment
the scene into objects and stationary `background'. This allows us to represent
the scene using a time-dependent (dynamic) map, in which each object is
explicitly represented as a separate instance and reconstructed in its own
volume. For each time step, our dynamic map maintains a relative pose of each
volume with respect to the stationary background. Our system operates in
incremental manner which is essential for on-line reconstruction, handles
large-scale environments with objects at large distances and runs in (near)
real-time. We demonstrate the efficacy of our approach on the KITTI dataset,
and provide qualitative and quantitative results showing high-quality dense 3D
reconstructions of a number of dynamic scenes.

该研究提出了一种实时重建大规模戶外动态环境的端到端系统，可以在维持追踪动态物体姿态信息和保证实时运行的情况下，提供具有高质量的密集 3D 重建效果。