Scene coordinate regression (SCR) methods are a family of visual localization
methods that directly regress 2D-3D matches for camera pose estimation. They
are effective in small-scale scenes but face significant challenges in
large-scale scenes that are further amplified in the absence of ground truth 3D
point clouds for supervision. Here, the model can only rely on reprojection
constraints and needs to implicitly triangulate the points. The challenges stem
from a fundamental dilemma: The network has to be invariant to observations of
the same landmark at different viewpoints and lighting conditions, etc., but at
the same time discriminate unrelated but similar observations. The latter
becomes more relevant and severe in larger scenes. In this work, we tackle this
problem by introducing the concept of co-visibility to the network. We propose
GLACE, which integrates pre-trained global and local encodings and enables SCR
to scale to large scenes with only a single small-sized network. Specifically,
we propose a novel feature diffusion technique that implicitly groups the
reprojection constraints with co-visibility and avoids overfitting to trivial
solutions. Additionally, our position decoder parameterizes the output
positions for large-scale scenes more effectively. Without using 3D models or
depth maps for supervision, our method achieves state-of-the-art results on
large-scale scenes with a low-map-size model. On Cambridge landmarks, with a
single model, we achieve 17% lower median position error than Poker, the
ensemble variant of the state-of-the-art SCR method ACE. Code is available at:
this https URL

我们提出了一个名为 GLACE 的方法，通过引入共视性的概念，利用预训练的全局和局部编码将场景坐标回归方法扩展到大型场景，只需要一个小型网络。我们的方法在大型场景中不使用三维模型或深度图作为监督，取得了最先进的结果。

GLACE: 全球局部加速坐标编码

GLACE: Global Local Accelerated Coordinate Encoding

We present a method to infer a dense depth map from a color image and
associated sparse depth measurements. Our main contribution lies in the design
of an annealing process for determining co-visibility (occlusions,
disocclusions) and the degree of regularization to impose on the model. We show
that regularization and co-visibility are related via the fitness (residual) of
model to data and both can be unified into a single framework to improve the
learning process. Our method is an adaptive weighting scheme that guides
optimization by measuring the residual at each pixel location over each
training step for (i) estimating a soft visibility mask and (ii) determining
the amount of regularization. We demonstrate the effectiveness our method by
applying it to several recent unsupervised depth completion methods and
improving their performance on public benchmark datasets, without incurring
additional trainable parameters or increase in inference time. Code available
at: this https URL

我们提出了一种方法来从彩色图像和相关稀疏深度测量中推断出密集的深度图。我们采用自适应加权方案，结合正则化和共视性关系，利用每个像素点的残差来指导优化过程，以提高训练性能。该方法有效地应用于多种无监督深度完成方法，并在公共基准数据集上改善其性能，不会产生额外的可训练参数或增加推理时间。