Abstract. The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that
explain an observation, most are implausible and do not correspond to
natural objects. Recent research in the field has tackled this problem by
exploiting the expressiveness of deep convolutional networks. In fact, there
is another level of ambiguity that is often overlooked: among plausible
shapes, there are still multiple shapes that fit the 2D image equally well;
i.e., the ground truth shape is non-deterministic given a single-view input.
Existing fully supervised approaches fail to address this issue, and often
produce blurry mean shapes with smooth surfaces but no fine details.
In this paper, we propose ShapeHD, pushing the limit of single-view shape
completion and reconstruction by integrating deep generative models
with adversarially learned shape priors. The learned priors serve as a
regularizer, penalizing the model only if its output is unrealistic, not if it
deviates from the ground truth. Our design thus overcomes both levels
of ambiguity aforementioned. Experiments demonstrate that ShapeHD
outperforms state of the art by a large margin in both shape completion
and shape reconstruction on multiple real datasets