  3D instance segmentation plays a crucial role in comprehending 3D scenes.
Despite recent advancements in this field, existing approaches exhibit certain
limitations. These methods often rely on fixed instance positions obtained from
sampled representative points in vast 3D point clouds, using center prediction
or farthest point sampling. However, these selected positions may deviate from
actual instance centers, posing challenges in precisely grouping instances.
Moreover, the common practice of grouping candidate instances from a single
type of coordinates introduces difficulties in identifying neighboring
instances or incorporating edge points. To tackle these issues, we present a
novel Transformer-based architecture, EipFormer, which comprises progressive
aggregation and dual position embedding. The progressive aggregation mechanism
leverages instance positions to refine instance proposals. It enhances the
initial instance positions through weighted farthest point sampling and further
refines the instance positions and proposals using aggregation averaging and
center matching. Additionally, dual position embedding superposes the original
and centralized position embeddings, thereby enhancing the model performance in
distinguishing adjacent instances. Extensive experiments on popular datasets
demonstrate that EipFormer achieves superior or comparable performance compared
to state-of-the-art approaches.
