  Point cloud semantic segmentation plays an essential role in autonomous
driving, providing vital information about drivable surfaces and nearby objects
that can aid higher level tasks such as path planning and collision avoidance.
While current 3D semantic segmentation networks focus on convolutional
architectures that perform great for well represented classes, they show a
significant drop in performance for underrepresented classes that share similar
geometric features. We propose a novel Detection Aware 3D Semantic Segmentation
(DASS) framework that explicitly leverages localization features from an
auxiliary 3D object detection task. By utilizing multitask training, the shared
feature representation of the network is guided to be aware of per class
detection features that aid tackling the differentiation of geometrically
similar classes. We additionally provide a pipeline that uses DASS to generate
high recall proposals for existing 2-stage detectors and demonstrate that the
added supervisory signal can be used to improve 3D orientation estimation
capabilities. Extensive experiments on both the SemanticKITTI and KITTI object
datasets show that DASS can improve 3D semantic segmentation results of
geometrically similar classes up to 37.8% IoU in image FOV while maintaining
high precision bird's-eye view (BEV) detection results.
