  In recent years, huge progress has been made on learning neural implicit
representations from multi-view images for 3D reconstruction. As an additional
input complementing coordinates, using sinusoidal functions as positional
encodings plays a key role in revealing high frequency details with
coordinate-based neural networks. However, high frequency positional encodings
make the optimization unstable, which results in noisy reconstructions and
artifacts in empty space. To resolve this issue in a general sense, we
introduce to learn neural implicit representations with quantized coordinates,
which reduces the uncertainty and ambiguity in the field during optimization.
Instead of continuous coordinates, we discretize continuous coordinates into
discrete coordinates using nearest interpolation among quantized coordinates
which are obtained by discretizing the field in an extremely high resolution.
We use discrete coordinates and their positional encodings to learn implicit
functions through volume rendering. This significantly reduces the variations
in the sample space, and triggers more multi-view consistency constraints on
intersections of rays from different views, which enables to infer implicit
function in a more effective way. Our quantized coordinates do not bring any
computational burden, and can seamlessly work upon the latest methods. Our
evaluations under the widely used benchmarks show our superiority over the
state-of-the-art. Our code is available at
https://github.com/MachinePerceptionLab/CQ-NIR.
