  We propose a novel approach aimed at object and semantic scene completion
from a partial scan represented as a 3D point cloud. Our architecture relies on
three novel layers that are used successively within an encoder-decoder
structure and specifically developed for the task at hand. The first one
carries out feature extraction by matching the point features to a set of
pre-trained local descriptors. Then, to avoid losing individual descriptors as
part of standard operations such as max-pooling, we propose an alternative
neighbor-pooling operation that relies on adopting the feature vectors with the
highest activations. Finally, up-sampling in the decoder modifies our feature
extraction in order to increase the output dimension. While this model is
already able to achieve competitive results with the state of the art, we
further propose a way to increase the versatility of our approach to process
point clouds. To this aim, we introduce a second model that assembles our
layers within a transformer architecture. We evaluate both architectures on
object and indoor scene completion tasks, achieving state-of-the-art
performance.
