Motivated by the need for estimating the 3D pose of arbitrary objects, we
consider the challenging problem of class-agnostic object viewpoint estimation
from images only, without CAD model knowledge. The idea is to leverage features
learned on seen classes to estimate the pose for classes that are unseen, yet
that share similar geometries and canonical frames with seen classes. We train
a direct pose estimator in a class-agnostic way by sharing weights across all
object classes, and we introduce a contrastive learning method that has three
main ingredients: (i) the use of pre-trained, self-supervised, contrast-based
features; (ii) pose-aware data augmentations; (iii) a pose-aware contrastive
loss. We experimented on Pascal3D+, ObjectNet3D and Pix3D in a cross-dataset
fashion, with both seen and unseen classes. We report state-of-the-art results,
including against methods that additionally use CAD models as input.

研究利用对已知物体类别学习到的特征来估算未知类别物体的三维姿态，提出无需 CAD 模型知识的类不可知对象视角估计的对比学习方法，实验结果表明该方法在多个数据集上具有最佳表现，包括使用 CAD 模型作为输入的方法。

PoseContrast: 基于姿态感知对比学习的野外无类别物体视角估计

PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild  with Pose-Aware Contrastive Learning

Data seems cheap to get, and in many ways it is, but the process of creating
a high quality labeled dataset from a mass of data is time-consuming and
expensive.
With the advent of rich 3D repositories, photo-realistic rendering systems
offer the opportunity to provide nearly limitless data. Yet, their primary
value for visual learning may be the quality of the data they can provide
rather than the quantity. Rendering engines offer the promise of perfect labels
in addition to the data: what the precise camera pose is; what the precise
lighting location, temperature, and distribution is; what the geometry of the
object is.
In this work we focus on semi-automating dataset creation through use of
synthetic data and apply this method to an important task -- object viewpoint
estimation. Using state-of-the-art rendering software we generate a large
labeled dataset of cars rendered densely in viewpoint space. We investigate the
effect of rendering parameters on estimation performance and show realism is
important. We show that generalizing from synthetic data is not harder than the
domain adaptation required between two real-image datasets and that combining
synthetic images with a small amount of real data improves estimation accuracy.

研究利用合成数据半自动化创建数据集并将其应用于对象视角估计这一重要任务，使用最先进的渲染软件在视点空间中密集地呈现图像，研究渲染参数对估计性能的影响，并表明合成数据的泛化不比两个真实图像数据集之间的领域适应更困难，并且将合成图像与少量真实数据结合使用可以提高估计准确性。

照片般逼真的渲染对视觉学习有多大帮助？

How useful is photo-realistic rendering for visual learning?

Object viewpoint estimation from 2D images is an essential task in computer
vision. However, two issues hinder its progress: scarcity of training data with
viewpoint annotations, and a lack of powerful features. Inspired by the growing
availability of 3D models, we propose a framework to address both issues by
combining render-based image synthesis and CNNs. We believe that 3D models have
the potential in generating a large number of images of high variation, which
can be well exploited by deep CNN with a high learning capacity. Towards this
goal, we propose a scalable and overfit-resistant image synthesis pipeline,
together with a novel CNN specifically tailored for the viewpoint estimation
task. Experimentally, we show that the viewpoint estimation from our pipeline
can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.

本研究提出了一种基于渲染图像合成和 CNN 的三维模型视点估计框架，通过构建可扩展且抗过拟合的图像合成流水线和针对视点估计任务的新型 CNN，实现了对 PASCAL 3D + 基准测试的最先进方法的显着优化