SupplementaryMaterial: JointLearningof2D-3D WeaklySupervisedSemanticSegmentation HyeokjunKweon KAIST 0327june@kaist.ac.kr Kuk-JinYoon KAIST kjyoon@kaist.ac.kr 1 Implementationdetails

In the first phase, we individually train both 2D and 3D classifiers with the classification loss of each domain. Here, in the second phase, please note that we first train our framework without the 3D-to-2D loss for the first few epochs. Here, weempirically observethat using abigger patch size isineffectiveinterms ofclassification, since having awider relative receptive field is crucial for understanding the scene from the image. On the other hand, when we use asmaller patch size, fine details ofthe image could not be preserved. Also, we filter the points ofthe occluded object which should not exist onthe image.