Associatively Segmenting Instances and Semantics in Point Clouds (1902.09852v2)

Abstract: A 3D point cloud describes the real scene precisely and intuitively.To date how to segment diversified elements in such an informative 3D scene is rarely discussed. In this paper, we first introduce a simple and flexible framework to segment instances and semantics in point clouds simultaneously. Then, we propose two approaches which make the two tasks take advantage of each other, leading to a win-win situation. Specifically, we make instance segmentation benefit from semantic segmentation through learning semantic-aware point-level instance embedding. Meanwhile, semantic features of the points belonging to the same instance are fused together to make more accurate per-point semantic predictions. Our method largely outperforms the state-of-the-art method in 3D instance segmentation along with a significant improvement in 3D semantic segmentation. Code has been made available at: https://github.com/WXinlong/ASIS.

• The paper introduces a unified framework that jointly optimizes instance and semantic segmentation tasks in point clouds, leading to improved accuracy.
• The method leverages semantic-aware instance embeddings and instance fusion techniques to effectively separate closely situated, diverse instances.
• The approach achieves significant gains over SGPN benchmarks on S3DIS and ShapeNet, offering both faster processing and superior segmentation performance.

An Essay on "Associatively Segmenting Instances and Semantics in Point Clouds"

The paper "Associatively Segmenting Instances and Semantics in Point Clouds" by Wang et al. introduces a novel framework, termed ASIS, designed to tackle instance and semantic segmentation tasks in point clouds simultaneously. The framework's primary innovation lies in its ability to allow these segmentation tasks to benefit mutually from their respective results, providing notable gains in accuracy and processing efficiency compared to existing state-of-the-art methods.

Core Contributions

1. Unified Framework for Instance and Semantic Segmentation: The proposed framework leverages two parallel branches to process instance and semantic information concurrently. This integration enables the model to achieve better performance collectively for both segmentation tasks, rather than treating them as isolated problems.
2. Semantic-Aware Instance Embedding: The introduction of semantic awareness into instance embedding is a key component of ASIS. By integrating semantic features into the instance embedding learning process, the model successfully differentiates among instances of varying classes more clearly. This leads to significant modifications in how 3D points are clustered in the embedding space, evidenced by the improved separation of close yet categorically different instances.
3. Instance-Fused Semantic Predictions: The framework also incorporates an instance-fusion mechanism that aggregates semantic features of neighboring points within the same instance embedding space. This not only enhances the per-point semantic predictions by leveraging the contextual information of the whole instance but also aligns well with how semantic coherence within instances boosts segmentation performance.

Results and Implications

The proposed method substantially outperforms the existing SGPN benchmark in terms of both instance and semantic segmentation on the S3DIS and ShapeNet datasets. For instance segmentation on S3DIS, ASIS achieves a marked improvement in mWCov across various test folds, indicating its superiority in identifying and distinguishing between instances. Semantic segmentation also sees notable enhancement in metrics like mIoU, demonstrating that better instance predictions naturally lead to improved semantic labeling.

One of the standout aspects of ASIS is its computational efficiency. It manifests a significant reduction in inference time compared to SGPN while concurrently advancing segmentation accuracy. This positions ASIS as a practical solution for real-world applications where rapid processing and high precision are crucial, such as in the fields of autonomous driving and AR/VR systems.

Theoretical and Practical Implications

Theoretically, the paper provides a valuable insight into the interplay between instance and semantic segmentation tasks. The mutual benefits as outlined by ASIS techniques open new avenues for research into co-segmentation strategies, particularly in complex and dense data representations like point clouds.

Practically, the framework can be extended to broader applicational domains, such as panoptic segmentation, where a unified treatment of foreground objects and background stuff segmentation is needed. Given the framework’s ability to adopt different backbone architectures, future extensions of this work may explore its integration with more advanced deep learning models to push the boundaries of current segmentation capabilities.

Overall, "Associatively Segmenting Instances and Semantics in Point Clouds" presents a compelling case for a holistic approach to segmentation problems, reaffirming the notion that segmentation tasks, when treated inseparably, can yield more robust and reliable results. The release of associated code further underscores the paper's contribution to advancing research in 3D computer vision.