The base model takes surround-view images of the ego-vehicle as input. As shown in Figure 1, we provide further visual comparisons of HD map vectorization results. The results reaffirm the necessity of a rasterization perspective in map vectorization. Figure 1 presents more visualization of MapVR's HD map construction results. As discussed in Section 3, the Chamfer-distance-based metric struggles to offer a fair evaluation for such scenarios.

MapVR (Map V ectorization via Rasterization), a novel framework that applies differentiable rasterization to vectorized outputs and then performs precise and geometry-aware supervision on rasterized HD maps.

The goal is to recover the clusters with minimum number of such queries. This problem has been of recent interest for the case of disjoint clusters.

Experimental data does not suffer from confounding but is usually limited in both scope and scale.

Almost Linearity T argeting, based on medium-scale almost linearity assumptions.

Weconsider the pool-based activelearning problem, where only asubset ofthe training data is labeled, and the goal is to query a batch of unlabeled samples to be labeled so as to maximally improve model performance.