  We introduce a new diffusion-based approach for shape completion on 3D range
scans. Compared with prior deterministic and probabilistic methods, we strike a
balance between realism, multi-modality, and high fidelity. We propose
DiffComplete by casting shape completion as a generative task conditioned on
the incomplete shape. Our key designs are two-fold. First, we devise a
hierarchical feature aggregation mechanism to inject conditional features in a
spatially-consistent manner. So, we can capture both local details and broader
contexts of the conditional inputs to control the shape completion. Second, we
propose an occupancy-aware fusion strategy in our model to enable the
completion of multiple partial shapes and introduce higher flexibility on the
input conditions. DiffComplete sets a new SOTA performance (e.g., 40% decrease
on l_1 error) on two large-scale 3D shape completion benchmarks. Our completed
shapes not only have a realistic outlook compared with the deterministic
methods but also exhibit high similarity to the ground truths compared with the
probabilistic alternatives. Further, DiffComplete has strong generalizability
on objects of entirely unseen classes for both synthetic and real data,
eliminating the need for model re-training in various applications.
