Overview of our concatenation-based domain adaptation (ConDA) framework. After preprocessing, sample stripes from both domains
are mixed via RV concatenation. The concatenated inputs are fed into the segmentation network for feature extraction. We include anti-aliasing
regularizers inside convolution operations to suppress the learning of high-frequency aliasing artifacts. The segmented RV cells are then projected
back to the point clouds. Here the target prediction part is omitted for simplicity. To mitigate the impediment caused by erroneous target predictions, we
design an entropy aggregator which splits samples into a confident set and an unsure set and disables the usage of samples from the latter set.