We present LaLaLoc to localise in environments without the need for prior
visitation, and in a manner that is robust to large changes in scene
appearance, such as a full rearrangement of furniture. Specifically, LaLaLoc
performs localisation through latent representations of room layout. LaLaLoc
learns a rich embedding space shared between RGB panoramas and layouts inferred
from a known floor plan that encodes the structural similarity between
locations. Further, LaLaLoc introduces direct, cross-modal pose optimisation in
its latent space. Thus, LaLaLoc enables fine-grained pose estimation in a scene
without the need for prior visitation, as well as being robust to dynamics,
such as a change in furniture configuration. We show that in a domestic
environment LaLaLoc is able to accurately localise a single RGB panorama image
to within 8.3cm, given only a floor plan as a prior.

LaLaLoc 利用房间布局的潜在表示进行本地化，通过潜在空间中的直接交叉模态姿态优化实现对全景图的细粒度姿态估计，从而在没有先前访问的情况下弥补了其它方法的缺陷，具有鲁棒性和较高的准确性。

动态未访问环境下的潜在布局定位 - LaLaLoc

LaLaLoc: Latent Layout Localisation in Dynamic, Unvisited Environments

Understanding and extracting 3D information of objects from monocular 2D
images is a fundamental problem in computer vision. In the task of 3D object
pose estimation, recent data driven deep neural network based approaches suffer
from scarcity of real images with 3D keypoint and pose annotations. Drawing
inspiration from human cognition, where the annotators use a 3D CAD model as
structural reference to acquire ground-truth viewpoints for real images; we
propose an iterative Semantic Pose Alignment Network, called iSPA-Net. Our
approach focuses on exploiting semantic 3D structural regularity to solve the
task of fine-grained pose estimation by predicting viewpoint difference between
a given pair of images. Such image comparison based approach also alleviates
the problem of data scarcity and hence enhances scalability of the proposed
approach for novel object categories with minimal annotation. The fine-grained
object pose estimator is also aided by correspondence of learned spatial
descriptor of the input image pair. The proposed pose alignment framework
enjoys the faculty to refine its initial pose estimation in consecutive
iterations by utilizing an online rendering setup along with effectiveness of a
non-uniform bin classification of pose-difference. This enables iSPA-Net to
achieve state-of-the-art performance on various real image viewpoint estimation
datasets. Further, we demonstrate effectiveness of the approach for multiple
applications. First, we show results for active object viewpoint localization
to capture images from similar pose considering only a single image as pose
reference. Second, we demonstrate the ability of the learned semantic
correspondence to perform unsupervised part-segmentation transfer using only a
single part-annotated 3D template model per object class. To encourage
reproducible research, we have released the codes for our proposed algorithm.

iSPA-Net is an iterative Semantic Pose Alignment Network that exploits semantic 3D structural regularity to solve the task of fine-grained pose estimation by predicting viewpoint difference between a given pair of images, and achieves state-of-the-art performance on various real image viewpoint estimation datasets with the aid of correspondence of learned spatial descriptor of the input image pair and refinement in consecutive iterations utilizing an online rendering setup along with effectiveness of a non-uniform bin classification of pose-difference; the approach also shows effectiveness for active object viewpoint localization and unsupervised part-segmentation transfer using only a single part-annotated 3D template model per object class.