ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution (2303.00246v2)

Abstract: Existing 3D instance segmentation methods are predominated by the bottom-up design -- manually fine-tuned algorithm to group points into clusters followed by a refinement network. However, by relying on the quality of the clusters, these methods generate susceptible results when (1) nearby objects with the same semantic class are packed together, or (2) large objects with loosely connected regions. To address these limitations, we introduce ISBNet, a novel cluster-free method that represents instances as kernels and decodes instance masks via dynamic convolution. To efficiently generate high-recall and discriminative kernels, we propose a simple strategy named Instance-aware Farthest Point Sampling to sample candidates and leverage the local aggregation layer inspired by PointNet++ to encode candidate features. Moreover, we show that predicting and leveraging the 3D axis-aligned bounding boxes in the dynamic convolution further boosts performance. Our method set new state-of-the-art results on ScanNetV2 (55.9), S3DIS (60.8), and STPLS3D (49.2) in terms of AP and retains fast inference time (237ms per scene on ScanNetV2). The source code and trained models are available at https://github.com/VinAIResearch/ISBNet.

ISBNet: A Novel Architecture for High-Performance 3D Point Cloud Instance Segmentation

The paper "ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution" offers an innovative perspective on 3D instance segmentation by introducing several strategic advancements over existing methodologies. ISBNet addresses the inherent challenges posed by traditional bottom-up segmentation methods, which rely on clustering algorithms that are sensitive to dense object proximity and loose intra-object connectivity, leading to inaccurate instance grouping.

Key Contributions

1. Cluster-Free Approach: Unlike conventional bottom-up designs, ISBNet employs a cluster-free methodology. The network defines instances through kernels, subsequently utilizing dynamic convolution to decode instance masks. This approach eliminates reliance on cluster quality, inherently reducing error propagation in dense scenarios or for large, loosely connected objects.
2. Instance-aware Farthest Point Sampling: A novel algorithm, Instance-aware Farthest Point Sampling (IA-FPS), is introduced to enhance sampling recall. This strategy ensures efficient candidate sampling by understanding spatial instance distributions, leading to more discriminative kernel generation.
3. Box-aware Dynamic Convolution: ISBNet incorporates 3D axis-aligned bounding boxes as auxiliary inputs to its dynamic convolution process. This innovation adds a geometric perspective that enhances mask prediction accuracy by leveraging bounding boxes as a spatial coherence cue.
4. Performance Benchmarks: The method achieves state-of-the-art results across notable datasets—ScanNetV2, S3DIS, and STPLS3D—surpassing previous approaches both in accuracy terms and computational efficiency. For instance, ISBNet reports an AP score of 55.9 on ScanNetV2, outperforming prior leading techniques.

Technical Insights

• Dynamic Convolution Enhancements: The integration of bounding box predictions introduces a geometric dimension that complements standard appearance features. This enhancement addresses scenarios where visually similar points require additional distinguishing factors, which are naturally provided by shape and spatial orientation.
• Efficiency Advancements: By avoiding clustering reliance, ISBNet reduces computational overhead and supports faster inference times, with 237ms per scene on ScanNetV2. This efficiency is nuanced by the network's streamlined encoder-decoder design maximizing the throughput of the dynamic convolutional network.

Implications and Future Directions

ISBNet's contributions present notable implications for applications requiring precise 3D segmentation, such as autonomous driving and augmented reality. The cluster-free approach and the enhanced use of bounding box predictions pave the way for further exploration in geometric-appearance integrated frameworks. Future developments might delve into extending these strategies to more complex datasets or integrating additional geometrical parameters (e.g., surface normals or object symmetry) to enjoy broader applicability and enhanced robustness.

Furthermore, as benchmarks evolve and datasets grow in complexity, ISBNet's modular architecture affords it adaptability to new challenges. The future scope of instance segmentation in point clouds could see ISBNet inspiring augmentations around multi-modal data integration, exploiting RGB-D or LiDAR data, to further improve segmentation accuracy and efficacy across diverse real-world scenarios.

In summary, ISBNet stands as a comprehensive framework that unifies sampling, encoding, and decoding under novel paradigms, demonstrating that thoughtful architectural changes can significantly advance 3DIS capabilities. This work represents a pivotal step towards more reliable and efficient methods in 3D vision applications.