  Recent advances show that semi-supervised implicit representation learning
can be achieved through physical constraints like Eikonal equations. However,
this scheme has not yet been successfully used for LiDAR point cloud data, due
to its spatially varying sparsity. In this paper, we develop a novel
formulation that conditions the semi-supervised implicit function on localized
shape embeddings. It exploits the strong representation learning power of
sparse convolutional networks to generate shape-aware dense feature volumes,
while still allows semi-supervised signed distance function learning without
knowing its exact values at free space. With extensive quantitative and
qualitative results, we demonstrate intrinsic properties of this new learning
system and its usefulness in real-world road scenes. Notably, we improve IoU
from 26.3% to 51.0% on SemanticKITTI. Moreover, we explore two paradigms to
integrate semantic label predictions, achieving implicit semantic completion.
Code and models can be accessed at https://github.com/OPEN-AIR-SUN/SISC.
