  3D point clouds are rich in geometric structure information, while 2D images
contain important and continuous texture information. Combining 2D information
to achieve better 3D semantic segmentation has become mainstream in 3D scene
understanding. Albeit the success, it still remains elusive how to fuse and
process the cross-dimensional features from these two distinct spaces. Existing
state-of-the-art usually exploit bidirectional projection methods to align the
cross-dimensional features and realize both 2D & 3D semantic segmentation
tasks. However, to enable bidirectional mapping, this framework often requires
a symmetrical 2D-3D network structure, thus limiting the network's flexibility.
Meanwhile, such dual-task settings may distract the network easily and lead to
over-fitting in the 3D segmentation task. As limited by the network's
inflexibility, fused features can only pass through a decoder network, which
affects model performance due to insufficient depth. To alleviate these
drawbacks, in this paper, we argue that despite its simplicity, projecting
unidirectionally multi-view 2D deep semantic features into the 3D space aligned
with 3D deep semantic features could lead to better feature fusion. On the one
hand, the unidirectional projection enforces our model focused more on the core
task, i.e., 3D segmentation; on the other hand, unlocking the bidirectional to
unidirectional projection enables a deeper cross-domain semantic alignment and
enjoys the flexibility to fuse better and complicated features from very
different spaces. In joint 2D-3D approaches, our proposed method achieves
superior performance on the ScanNetv2 benchmark for 3D semantic segmentation.
