In recent years, estimating the 6D pose of object instances with
convolutional neural network (CNN) has received considerable attention.
Depending on whether intermediate cues are used, the relevant literature can be
roughly divided into two broad categories: direct methods and two stage
pipelines. For the latter, intermediate cues, such as 3D object coordinates,
semantic keypoints, or virtual control points instead of pose parameters are
regressed by CNN in the first stage. Object pose can then be solved by
correspondence constraints constructed with these intermediate cues. In this
paper, we focus on the postprocessing of a two-stage pipeline and propose to
combine two learning concepts for estimating object pose under challenging
scenes: projection grouping on one side, and correspondence learning on the
other. We firstly employ a local patch based method to predict projection
heatmaps which denote the confidence distribution of projection of 3D bounding
box's corners. A projection grouping module is then proposed to remove
redundant local maxima from each layer of heatmaps. Instead of directly feeding
2D-3D correspondences to the perspective-n-point (PnP) algorithm, multiple
correspondence hypotheses are sampled from local maxima and its corresponding
neighborhood and ranked by a correspondence-evaluation network. Finally,
correspondences with higher confidence are selected to determine object pose.
Extensive experiments on three public datasets demonstrate that the proposed
framework outperforms several state of the art methods.

本文提出一种基于卷积神经网络的 2 阶段模型，采用投影分组和对应关系学习方法预测物体位姿，优于现有的其他方法。

DeepHMap++：结合投影分组和对应关系学习的全自由度姿态估计

DeepHMap++: Combined Projection Grouping and Correspondence Learning for  Full DoF Pose Estimation

We present a system for learning motion of independently moving objects from
stereo videos. The only human annotation used in our system are 2D object
bounding boxes which introduce the notion of objects to our system. Unlike
prior learning based work which has focused on predicting dense pixel-wise
optical flow field and/or a depth map for each image, we propose to predict
object instance specific 3D scene flow maps and instance masks from which we
are able to derive the motion direction and speed for each object instance. Our
network takes the 3D geometry of the problem into account which allows it to
correlate the input images. We present experiments evaluating the accuracy of
our 3D flow vectors, as well as depth maps and projected 2D optical flow where
our jointly learned system outperforms earlier approaches trained for each task
independently.

该论文提出了一个从立体视频中学习独立移动物体动作的系统，该系统使用的唯一人工注释是 2D 物体边界框，并从中引入了对象的概念。与以前的基于学习的工作不同，该工作集中于预测每个图像的密集像素光流场和 / 或深度图，我们提出了从物体实例特定的 3D 场景流图和实例掩模预测来推断每个物体实例的运动方向和速度的方法，并且我们的网络考虑了问题的 3D 几何，允许它相关输入图像。我们进行了实验，评估了我们的 3D 流向量、深度图和投影的 2D 光流的精度，在这些实验中，我们联合学习的系统优于先前独立训练每个任务的方法。

从未标注的立体视频中学习独立的物体运动

Learning Independent Object Motion from Unlabelled Stereoscopic Videos

We introduce count-guided weakly supervised localization (C-WSL), an approach
that uses per-class object count as a new form of supervision to improve weakly
supervised localization (WSL). C-WSL uses a simple count-based region selection
algorithm to select high-quality regions, each of which covers a single object
instance during training, and improves existing WSL methods by training with
the selected regions. To demonstrate the effectiveness of C-WSL, we integrate
it into two WSL architectures and conduct extensive experiments on VOC2007 and
VOC2012. Experimental results show that C-WSL leads to large improvements in
WSL and that the proposed approach significantly outperforms the
state-of-the-art methods. The results of annotation experiments on VOC2007
suggest that a modest extra time is needed to obtain per-class object counts
compared to labeling only object categories in an image. Furthermore, we reduce
the annotation time by more than $2\times$ and $38\times$ compared to
center-click and bounding-box annotations.

本文介绍了计数引导的弱监督定位 (C-WSL)，使用每类物体数量作为一种新的监督形式来改善弱监督定位 (WSL)。C-WSL 使用一种简单的基于计数的区域选择算法来选择高质量的区域，每个区域在训练期间覆盖一个单独的对象实例，并通过训练选择的区域来提高现有的 WSL 方法，实验结果表明，C-WSL 可以显著提高 WSL 性能，使其显著优于现有的最先进方法。同时，我们降低了注释时间，相对于中心点击和边界框注释，减少了超过 2 倍和 38 倍。