Matching landmark patches from a real-time image captured by an on-vehicle
camera with landmark patches in an image database plays an important role in
various computer perception tasks for autonomous driving. Current methods focus
on local matching for regions of interest and do not take into account spatial
neighborhood relationships among the image patches, which typically correspond
to objects in the environment. In this paper, we construct a spatial graph with
the graph vertices corresponding to patches and edges capturing the spatial
neighborhood information. We propose a joint feature and metric learning model
with graph-based learning. We provide a theoretical basis for the graph-based
loss by showing that the information distance between the distributions
conditioned on matched and unmatched pairs is maximized under our framework. We
evaluate our model using several street-scene datasets and demonstrate that our
approach achieves state-of-the-art matching results.

本文提出了一种基于空间图的联合特征和度量学习模型，构建了一个以图顶点表示贴片、以边捕捉空间邻域信息的空间图，并通过理论分析证明了我们框架下匹配和不匹配对应的条件分布之间的信息距离最大化，通过在多个街景数据集上的评估我们证明了该方法达到了最先进的匹配结果。

基于图形学习的街景图像补丁匹配

Image Patch-Matching with Graph-Based Learning in Street Scenes

The task of motion forecasting is critical for self-driving vehicles (SDVs)
to be able to plan a safe maneuver. Towards this goal, modern approaches reason
about the map, the agents' past trajectories and their interactions in order to
produce accurate forecasts. The predominant approach has been to encode the map
and other agents in the reference frame of each target agent. However, this
approach is computationally expensive for multi-agent prediction as inference
needs to be run for each agent. To tackle the scaling challenge, the solution
thus far has been to encode all agents and the map in a shared coordinate frame
(e.g., the SDV frame). However, this is sample inefficient and vulnerable to
domain shift (e.g., when the SDV visits uncommon states). In contrast, in this
paper, we propose an efficient shared encoding for all agents and the map
without sacrificing accuracy or generalization. Towards this goal, we leverage
pair-wise relative positional encodings to represent geometric relationships
between the agents and the map elements in a heterogeneous spatial graph. This
parameterization allows us to be invariant to scene viewpoint, and save online
computation by re-using map embeddings computed offline. Our decoder is also
viewpoint agnostic, predicting agent goals on the lane graph to enable diverse
and context-aware multimodal prediction. We demonstrate the effectiveness of
our approach on the urban Argoverse 2 benchmark as well as a novel highway
dataset.

本文提出了一种有效的多个自主行驶目标预测方法，该方法使用空间图形表示推理中各种几何关系，并节省在线计算通过离线计算得到的地图嵌入。