  Surface reconstruction from point clouds is vital for 3D computer vision.
State-of-the-art methods leverage large datasets to first learn local context
priors that are represented as neural network-based signed distance functions
(SDFs) with some parameters encoding the local contexts. To reconstruct a
surface at a specific query location at inference time, these methods then
match the local reconstruction target by searching for the best match in the
local prior space (by optimizing the parameters encoding the local context) at
the given query location. However, this requires the local context prior to
generalize to a wide variety of unseen target regions, which is hard to
achieve. To resolve this issue, we introduce Predictive Context Priors by
learning Predictive Queries for each specific point cloud at inference time.
Specifically, we first train a local context prior using a large point cloud
dataset similar to previous techniques. For surface reconstruction at inference
time, however, we specialize the local context prior into our Predictive
Context Prior by learning Predictive Queries, which predict adjusted spatial
query locations as displacements of the original locations. This leads to a
global SDF that fits the specific point cloud the best. Intuitively, the query
prediction enables us to flexibly search the learned local context prior over
the entire prior space, rather than being restricted to the fixed query
locations, and this improves the generalizability. Our method does not require
ground truth signed distances, normals, or any additional procedure of signed
distance fusion across overlapping regions. Our experimental results in surface
reconstruction for single shapes or complex scenes show significant
improvements over the state-of-the-art under widely used benchmarks.
