We introduce Open3DIS, a novel solution designed to tackle the problem of
Open-Vocabulary Instance Segmentation within 3D scenes. Objects within 3D
environments exhibit diverse shapes, scales, and colors, making precise
instance-level identification a challenging task. Recent advancements in
Open-Vocabulary scene understanding have made significant strides in this area
by employing class-agnostic 3D instance proposal networks for object
localization and learning queryable features for each 3D mask. While these
methods produce high-quality instance proposals, they struggle with identifying
small-scale and geometrically ambiguous objects. The key idea of our method is
a new module that aggregates 2D instance masks across frames and maps them to
geometrically coherent point cloud regions as high-quality object proposals
addressing the above limitations. These are then combined with 3D
class-agnostic instance proposals to include a wide range of objects in the
real world. To validate our approach, we conducted experiments on three
prominent datasets, including ScanNet200, S3DIS, and Replica, demonstrating
significant performance gains in segmenting objects with diverse categories
over the state-of-the-art approaches.

Open3DIS 是对 3D 场景中开放词汇实例分割问题的一种新解决方案，通过聚合帧间的 2D 实例掩码并将其映射到几何连贯的点云区域作为高质量的对象提案，与 3D 非特定类实例提案相结合，能够在真实世界的广泛对象中实现显著的性能提升。