In this paper, we propose a novel real-time 6D object pose estimation
framework, named G2L-Net. Our network operates on point clouds from RGB-D
detection in a divide-and-conquer fashion. Specifically, our network consists
of three steps. First, we extract the coarse object point cloud from the RGB-D
image by 2D detection. Second, we feed the coarse object point cloud to a
translation localization network to perform 3D segmentation and object
translation prediction. Third, via the predicted segmentation and translation,
we transfer the fine object point cloud into a local canonical coordinate, in
which we train a rotation localization network to estimate initial object
rotation. In the third step, we define point-wise embedding vector features to
capture viewpoint-aware information. To calculate more accurate rotation, we
adopt a rotation residual estimator to estimate the residual between initial
rotation and ground truth, which can boost initial pose estimation performance.
Our proposed G2L-Net is real-time despite the fact multiple steps are stacked
via the proposed coarse-to-fine framework. Extensive experiments on two
benchmark datasets show that G2L-Net achieves state-of-the-art performance in
terms of both accuracy and speed.

本文提出了一种新颖的 6D 物体姿态估计框架，称为 G2L-Net，它可以通过 RGB-D 检测处理点云数据来进行实时姿态估计，并在两个基准数据集上取得了最先进的性能。

G2L-Net：面向实时 6D 姿态估计的全局到局部网络与嵌入向量特征

G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with  Embedding Vector Features

Instance segmentation methods often require costly per-pixel labels. We
propose a method that only requires point-level annotations. During training,
the model only has access to a single pixel label per object, yet the task is
to output full segmentation masks. To address this challenge, we construct a
network with two branches: (1) a localization network (L-Net) that predicts the
location of each object; and (2) an embedding network (E-Net) that learns an
embedding space where pixels of the same object are close. The segmentation
masks for the located objects are obtained by grouping pixels with similar
embeddings. At training time, while L-Net only requires point-level
annotations, E-Net uses pseudo-labels generated by a class-agnostic object
proposal method. We evaluate our approach on PASCAL VOC, COCO, KITTI and
CityScapes datasets. The experiments show that our method (1) obtains
competitive results compared to fully-supervised methods in certain scenarios;
(2) outperforms fully- and weakly- supervised methods with a fixed annotation
budget; and (3) is a first strong baseline for instance segmentation with
point-level supervision.

该研究提出了一种只需要点级别注释来训练模型实现物体实例分割的方法，其通过建立一个具有两个分支（定位网络和嵌入网络）的网络，使用伪标签和相似嵌入方法来提高分割效果，在特定情况下，可以获得有竞争力的结果，并且是一种实例分割点级别监督的强基准线。

基于点监督的实例分割

Instance Segmentation with Point Supervision

We address temporal action localization in untrimmed long videos. This is
important because videos in real applications are usually unconstrained and
contain multiple action instances plus video content of background scenes or
other activities. To address this challenging issue, we exploit the
effectiveness of deep networks in temporal action localization via three
segment-based 3D ConvNets: (1) a proposal network identifies candidate segments
in a long video that may contain actions; (2) a classification network learns
one-vs-all action classification model to serve as initialization for the
localization network; and (3) a localization network fine-tunes on the learned
classification network to localize each action instance. We propose a novel
loss function for the localization network to explicitly consider temporal
overlap and therefore achieve high temporal localization accuracy. Only the
proposal network and the localization network are used during prediction. On
two large-scale benchmarks, our approach achieves significantly superior
performances compared with other state-of-the-art systems: mAP increases from
1.7% to 7.4% on MEXaction2 and increases from 15.0% to 19.0% on THUMOS 2014,
when the overlap threshold for evaluation is set to 0.5.

本研究提出了一种基于三种分段 3D 卷积神经网络的方法，用于解决未经修剪的长视频中的时间动作定位问题，其中提出网络用于识别可能包含动作的候选段，分类网络以一对多动作分类模型进行学习以作为定位网络的初始化，用于定位每个动作实例。