Hierarchical Aggregation for 3D Instance Segmentation (2108.02350v1)

Abstract: Instance segmentation on point clouds is a fundamental task in 3D scene perception. In this work, we propose a concise clustering-based framework named HAIS, which makes full use of spatial relation of points and point sets. Considering clustering-based methods may result in over-segmentation or under-segmentation, we introduce the hierarchical aggregation to progressively generate instance proposals, i.e., point aggregation for preliminarily clustering points to sets and set aggregation for generating complete instances from sets. Once the complete 3D instances are obtained, a sub-network of intra-instance prediction is adopted for noisy points filtering and mask quality scoring. HAIS is fast (only 410ms per frame) and does not require non-maximum suppression. It ranks 1st on the ScanNet v2 benchmark, achieving the highest 69.9% AP50 and surpassing previous state-of-the-art (SOTA) methods by a large margin. Besides, the SOTA results on the S3DIS dataset validate the good generalization ability. Code will be available at https://github.com/hustvl/HAIS.

Hierarchical Aggregation for 3D Instance Segmentation: An Expert Overview

The paper, "Hierarchical Aggregation for 3D Instance Segmentation," written by Shaoyu Chen et al., offers a novel approach to the challenging task of 3D instance segmentation on point clouds, which is foundational for 3D scene understanding—a crucial component in applications like robotics, autonomous driving, and augmented reality. The work introduces a clustering-based framework termed HAIS, which leverages a hierarchical approach to aggregate points into instances, thereby improving the segmentation performance on large-scale 3D datasets.

Key Contributions

The primary contribution of this research lies in the introduction of a hierarchical aggregation process that addresses common issues with clustering-based methods, such as over-segmentation or under-segmentation, by deploying a dual-step aggregation process: point aggregation and set aggregation. This hierarchical strategy crucially reorganizes point clouds at different levels, from individual points to larger sets, before arriving at complete instance predictions. The paper highlights the efficiency of HAIS, which reportedly processes a frame every 410ms, a significant improvement over existing methods.

An additional novel aspect is the incorporation of an intra-instance prediction network that refines the instance quality by filtering out noisy points, thereby enabling a robust scoring mechanism for mask quality. This scoring bypasses the need for non-maximum suppression explicitly, a typical requirement in the majority of instance segmentation methods.

Numerical Results and Comparisons

HAIS demonstrates a prominent performance, topping the ScanNet v2 benchmark with an $AP_{50}$ of 69.9%, significantly surpassing prior models. This achievement underscores the efficacy of HAIS's hierarchical strategy in harnessing spatial relationships within point data to improve segmentation accuracy. Furthermore, the robustness of the framework is validated across variations and challenging datasets such as S3DIS, indicating its adaptability and generalization potential.

Implications for AI Development

From a theoretical perspective, the hierarchical aggregation framework sets a new precedent for exploiting multi-level spatial relations in 3D data, which could inspire further research into segmentation methodologies that balance between computational efficiency and accuracy. Practically, the robust, efficient processing of 3D scenes can accelerate advancements in various real-world applications, especially those necessitating real-time object recognition and classification capabilities.

Future Directions

Future research can expand on this work by integrating HAIS with more diverse and larger datasets, investigating the impacts of different aggregation strategies within hierarchical frameworks, or adapting the methodology to other types of 3D data representations. Additionally, exploring the potential enhancements of intra-instance predictions could further refine instance quality, particularly in dynamic scenes with complex object interactions.

In conclusion, the proposed HAIS framework represents a significant step in the efficiency and effectiveness of 3D instance segmentation, with wide-reaching implications for the development of perception systems in AI. By breaking away from traditional clustering conventions, this research opens new avenues for exploration in 3D computer vision.