A single unexpected object on the road can cause an accident or may lead to
injuries. To prevent this, we need a reliable mechanism for finding anomalous
objects on the road. This task, called anomaly segmentation, can be a stepping
stone to safe and reliable autonomous driving. Current approaches tackle
anomaly segmentation by assigning an anomaly score to each pixel and by
grouping anomalous regions using simple heuristics. However, pixel grouping is
a limiting factor when it comes to evaluating the segmentation performance of
individual anomalous objects. To address the issue of grouping multiple anomaly
instances into one, we propose an approach that produces accurate anomaly
instance masks. Our approach centers on an out-of-distribution segmentation
model for identifying uncertain regions and a strong generalist segmentation
model for anomaly instances segmentation. We investigate ways to use uncertain
regions to guide such a segmentation model to perform segmentation of anomalous
instances. By incorporating strong object priors from a generalist model we
additionally improve the per-pixel anomaly segmentation performance. Our
approach outperforms current pixel-level anomaly segmentation methods,
achieving an AP of 80.08% and 88.98% on the Fishyscapes Lost and Found and the
RoadAnomaly validation sets respectively. Project page:
this https URL

通过使用不确定区域指导细分异常实例的模型和强大的对象先验，我们提出了一个能够准确检测道路异常物体的方法，优于当前的像素级异常分割方法，实验结果在 Fishyscapes Lost and Found 和 RoadAnomaly 验证集上分别达到了 80.08% 和 88.98% 的平均准确率。

UGainS: 不确定性引导的异常实例分割

UGainS: Uncertainty Guided Anomaly Instance Segmentation

Shape completion, i.e., predicting the complete geometry of an object from a
partial observation, is highly relevant for several downstream tasks, most
notably robotic manipulation. When basing planning or prediction of real grasps
on object shape reconstruction, an indication of severe geometric uncertainty
is indispensable. In particular, there can be an irreducible uncertainty in
extended regions about the presence of entire object parts when given ambiguous
object views. To treat this important case, we propose two novel methods for
predicting such uncertain regions as straightforward extensions of any method
for predicting local spatial occupancy, one through postprocessing occupancy
scores, the other through direct prediction of an uncertainty indicator. We
compare these methods together with two known approaches to probabilistic shape
completion. Moreover, we generate a dataset, derived from ShapeNet, of
realistically rendered depth images of object views with ground-truth
annotations for the uncertain regions. We train on this dataset and test each
method in shape completion and prediction of uncertain regions for known and
novel object instances and on synthetic and real data. While direct uncertainty
prediction is by far the most accurate in the segmentation of uncertain
regions, both novel methods outperform the two baselines in shape completion
and uncertain region prediction, and avoiding the predicted uncertain regions
increases the quality of grasps for all tested methods. Web:
this https URL

通过两种方法预测局部空间占用与直接确定性预测，提高不确定区域的分割准确性、完成物体形状，增加抓取质量。