Instance-Aware Embedding for Point Cloud Instance Segmentation

Although recent works have made significant progress in encoding meaningful context information for instance segmentation in 2D images, the works for 3D point cloud counterpart lag far behind. Conventional methods use radius search or other similar methods for aggregating local information. However, these methods are unaware of the instance context and fail to realize the boundary and geometric information of an instance, which are critical to separate adjacent objects. In this work, we study the influence of instance-aware knowledge by proposing an Instance-Aware Module (IAM). The proposed IAM learns discriminative instance embedding features in two-fold: (1) Instance contextual regions, covering the spatial extension of an instance, are implicitly learned and propagated in the decoding process. (2) Instance-dependent geometric knowledge is included in the embedding space, which is informative and critical to discriminate adjacent instances. Moreover, the proposed IAM is free from complex and time-consuming operations, showing superiority in both accuracy and efficiency over the previous methods. To validate the effectiveness of our proposed method, comprehensive experiments have been conducted on three popular benchmarks for instance segmentation: ScannetV2, S3DIS, and PartNet. The flexibility of our method allows it to handle both indoor scenes and CAD objects. We achieve state-of-the-art performance, outperforming previous methods substantially.

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