Efficient detection and description of geometric regions in images is a
prerequisite in visual systems for localization and mapping. Such systems still
rely on traditional hand-crafted methods for efficient generation of
lightweight descriptors, a common limitation of the more powerful neural
network models that come with high compute and specific hardware requirements.
In this paper, we focus on the adaptations required by detection and
description neural networks to enable their use in computationally limited
platforms such as robots, mobile, and augmented reality devices. To that end,
we investigate and adapt network quantization techniques to accelerate
inference and enable its use on compute limited platforms. In addition, we
revisit common practices in descriptor quantization and propose the use of a
binary descriptor normalization layer, enabling the generation of distinctive
binary descriptors with a constant number of ones. ZippyPoint, our efficient
quantized network with binary descriptors, improves the network runtime speed,
the descriptor matching speed, and the 3D model size, by at least an order of
magnitude when compared to full-precision counterparts. These improvements come
at a minor performance degradation as evaluated on the tasks of homography
estimation, visual localization, and map-free visual relocalization. Code and
models are available at this https URL

研究如何在计算能力有限的平台上使用检测和描述神经网络，探索和适应网络量化技术来提高推理速度，并建议使用二进制描述符规范化层，生成具有恒定数字的显著二进制描述符。通过 ZippyPoint，可以在与全精度模型相比，可显著提高网络运行速度、描述符匹配速度和 3D 模型大小的情况下增加一个数量级的性能。

ZippyPoint：通过混合精度离散化实现快速兴趣点检测、描述和匹配

ZippyPoint: Fast Interest Point Detection, Description, and Matching  through Mixed Precision Discretization

We describe a novel approach to image based localisation in urban
environments using semantic matching between images and a 2-D map. It contrasts
with the vast majority of existing approaches which use image to image database
matching. We use highly compact binary descriptors to represent semantic
features at locations, significantly increasing scalability compared with
existing methods and having the potential for greater invariance to variable
imaging conditions. The approach is also more akin to human map reading, making
it more suited to human-system interaction. The binary descriptors indicate the
presence or not of semantic features relating to buildings and road junctions
in discrete viewing directions. We use CNN classifiers to detect the features
in images and match descriptor estimates with a database of location tagged
descriptors derived from the 2-D map. In isolation, the descriptors are not
sufficiently discriminative, but when concatenated sequentially along a route,
their combination becomes highly distinctive and allows localisation even when
using non-perfect classifiers. Performance is further improved by taking into
account left or right turns over a route. Experimental results obtained using
Google StreetView and OpenStreetMap data show that the approach has
considerable potential, achieving localisation accuracy of around 85% using
routes corresponding to approximately 200 meters.

本文提出了一种新的基于图像的城市定位方法，使用图像与 2D 地图之间的语义匹配来表示语义特征，其中的二进制描述符能够更好地适应可变成像条件，而 CNN 分类器则用来检测图像的特征并与地图数据库中的信息进行匹配，这种方法相对于传统的图像数据库匹配更具有可扩展性和人机交互性.

基于二进制语义描述符的 2D 地图图像定位自动化

Automated Map Reading: Image Based Localisation in 2-D Maps Using Binary  Semantic Descriptors

We present a novel means of describing local image appearances using binary
strings. Binary descriptors have drawn increasing interest in recent years due
to their speed and low memory footprint. A known shortcoming of these
representations is their inferior performance compared to larger, histogram
based descriptors such as the SIFT. Our goal is to close this performance gap
while maintaining the benefits attributed to binary representations. To this
end we propose the Learned Arrangements of Three Patch Codes descriptors, or
LATCH. Our key observation is that existing binary descriptors are at an
increased risk from noise and local appearance variations. This, as they
compare the values of pixel pairs; changes to either of the pixels can easily
lead to changes in descriptor values, hence damaging its performance. In order
to provide more robustness, we instead propose a novel means of comparing pixel
patches. This ostensibly small change, requires a substantial redesign of the
descriptors themselves and how they are produced. Our resulting LATCH
representation is rigorously compared to state-of-the-art binary descriptors
and shown to provide far better performance for similar computation and space
requirements.

提出了 Learned Arrangements of Three Patch Codes descriptors (LATCH) 的方法，通过使用像素块的新颖比较方法，着眼于解决现有二进制描述符在处理噪声和局部外观变化时的性能问题，相比其他二进制描述符具有更好的性能表现。