Scalable SoftGroup for 3D Instance Segmentation on Point Clouds (2209.08263v3)

Abstract: This paper considers a network referred to as SoftGroup for accurate and scalable 3D instance segmentation. Existing state-of-the-art methods produce hard semantic predictions followed by grouping instance segmentation results. Unfortunately, errors stemming from hard decisions propagate into the grouping, resulting in poor overlap between predicted instances and ground truth and substantial false positives. To address the abovementioned problems, SoftGroup allows each point to be associated with multiple classes to mitigate the uncertainty stemming from semantic prediction. It also suppresses false positive instances by learning to categorize them as background. Regarding scalability, the existing fast methods require computational time on the order of tens of seconds on large-scale scenes, which is unsatisfactory and far from applicable for real-time. Our finding is that the $k$-Nearest Neighbor ($k$-NN) module, which serves as the prerequisite of grouping, introduces a computational bottleneck. SoftGroup is extended to resolve this computational bottleneck, referred to as SoftGroup++. The proposed SoftGroup++ reduces time complexity with octree $k$-NN and reduces search space with class-aware pyramid scaling and late devoxelization. Experimental results on various indoor and outdoor datasets demonstrate the efficacy and generality of the proposed SoftGroup and SoftGroup++. Their performances surpass the best-performing baseline by a large margin (6\% $\sim$ 16\%) in terms of AP$_{50}$. On datasets with large-scale scenes, SoftGroup++ achieves a 6$\times$ speed boost on average compared to SoftGroup. Furthermore, SoftGroup can be extended to perform object detection and panoptic segmentation with nontrivial improvements over existing methods. The source code and trained models are available at \url{https://github.com/thangvubk/SoftGroup}.

• The paper introduces a novel soft grouping mechanism that uses soft semantic scores to reduce error propagation in 3D instance segmentation.
• The paper presents SoftGroup++, which employs octree k-NN and pyramid scaling to lower computational complexity and achieve a 6× speed boost on large scenes.
• The paper validates its approach with improved accuracy and versatility across datasets like ScanNet v2 and SemanticKITTI, highlighting broad applicability in 3D vision tasks.

Scalable SoftGroup for 3D Instance Segmentation on Point Clouds

The paper "Scalable SoftGroup for 3D Instance Segmentation on Point Clouds" introduces a novel approach for effective and scalable instance segmentation in the context of 3D point clouds. The method, known as SoftGroup, addresses limitations found in existing state-of-the-art instance segmentation strategies by utilizing a soft grouping mechanism to enhance accuracy and a sophisticated architecture, SoftGroup++, to improve scalability on large-scale scenes.

Key Contributions

The work presents several noteworthy contributions:

1. SoftGroup Architecture: The authors tackle the problem of error propagation due to hard semantic predictions by introducing SoftGroup. The network associates each point with multiple classes using soft semantic scores, thereby mitigating uncertainty and reducing false positives by categorizing them as background. This soft association leads to more accurate segmentation results by preventing errors that commonly arise in hard-decision pipelines.
2. Scalability – SoftGroup++: Addressing the computational bottlenecks typical of large-scale point cloud data, the researchers introduce SoftGroup++. This version integrates octree $k$-NN to reduce time complexity from $\mathcal{O}(n^2)$ to $\mathcal{O}(n\log n)$. Furthermore, it employs class-aware pyramid scaling and late devoxelization to diminish search spaces during processing, leading to a significant speed boost.
3. Performance and Versatility: Experimental results across multiple datasets illustrate that SoftGroup and SoftGroup++ outperform their predecessors. They show substantial improvements in AP$_{50}$ scores, with SoftGroup++ attaining a 6$\times$ speed gain over SoftGroup on large-scale scenes. Remarkably, these methods can extend to object detection and panoptic segmentation, increasing their utility across different 3D vision tasks.

Experimental Insights

The experimental evaluation demonstrates the strengths of these methods:

• The SoftGroup architecture achieves substantial gains in accuracy, with improvements ranging from 6\% to 16\% over leading methods on datasets such as ScanNet v2.
• SoftGroup++ exhibits considerable improvements in processing large-scale datasets, evidenced by the significant reductions in inference time without sacrificing accuracy.
• The proposed methods demonstrate flexibility and superior performance in different 3D instance segmentation and detection contexts, as evidenced by their success across varied datasets like S3DIS, STPLS3D, and SemanticKITTI.

Theoretical and Practical Implications

The paper sheds light on the interplay between uncertainty in semantic predictions and instance grouping, advocating for a soft decision lens in machine learning pipelines for 3D point cloud data. Practically, the findings allow for near-real-time processing of large-point-cloud scenes, paving the way for more efficient applications in autonomous driving, robotics, and AR/VR environments where time-critical performance is essential.

Future Directions

Future research could explore optimizing the hyperparameters intrinsic to SoftGroup++ further, potentially incorporating more adaptive mechanisms for real-time applications. Additionally, extending the principles underlying SoftGroup to other forms of hierarchical and non-hierarchical data could prove beneficial, broadening the applicability of these insights within the broader artificial intelligence research community.

In conclusion, this paper delivers a robust framework enhancing both the scalability and accuracy in 3D instance segmentation, effectively bridging theoretical advancements with practical applications in AI-driven 3D perception tasks.