CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds (2210.04264v1)

Abstract: We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels with the same semantic predictions, which considers semantic consistency and diverse locality abandoned in previous bottom-up approaches. Then, to recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module to directly aggregate fine-grained spatial information from backbone for further proposal refinement. It is memory-and-computation efficient and can better encode the geometry-specific features of each 3D proposal. Our model achieves state-of-the-art 3D detection performance with remarkable gains of +\textit{3.6\%} on ScanNet V2 and +\textit{2.6}\% on SUN RGB-D in term of [email protected]. Code will be available at https://github.com/Haiyang-W/CAGroup3D.

• The paper introduces a two-stage framework that integrates class-aware grouping and dynamic voxel sizing to generate high-quality 3D proposals.
• It leverages a fully sparse convolutional backbone with RoI-Conv pooling to refine features and preserve spatial details in complex scenes.
• Experimental results on ScanNet V2 and SUN RGB-D demonstrate notable mAP improvements, underscoring its impact on autonomous and robotic applications.

CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds

The paper "CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds" introduces an innovative two-stage detection framework, CAGroup3D, designed for efficient and robust 3D object detection from point clouds. The framework enhances feature extraction and proposal generation processes with class-aware strategies, catering specifically to the semantic and geometric diversity inherent in different object classes.

Overview of Methodology

CAGroup3D employs a two-stage architecture, separating the processes of initial proposal generation and subsequent refinement. It integrates novel mechanisms to address the limitations of traditional class-agnostic approaches, particularly in cluttered environments where semantic overlaps and object size diversity challenge accurate detection.

Stage 1 - Proposal Generation:

1. Class-Aware Local Grouping:
   • The class-aware strategy begins with the generation of high-quality 3D proposals employing a grouping mechanism sensitive to semantic predictions. This approach contrasts with previous methods, which were agnostic to class distinctions, resulting in semantically inconsistent grouping.
   • This stage involves voxel-wise semantic prediction followed by selective grouping of voxel features based on predicted semantic consistency. A key innovation is the dynamically adaptive voxel size, tailored to class-specific average dimensions, which improves proposal accuracy by retaining class-suitable object boundaries.

2. Fully Sparse Convolutional Backbone: - The authors opt for a 3D sparse convolutional network to efficiently process large-scale point clouds. It maintains spatial resolution and increases computational efficiency, leveraging a BiResNet architecture to facilitate multi-resolution feature learning.

Stage 2 - Proposal Refinement:

1. RoI-Conv Pooling Module:
   • To counteract missed features during proposal generation due to errors in voxel-wise segmentation, a dense convolutional RoI pooling strategy is proposed. Traditional max-pooling techniques are replaced with fully sparse convolutions, retaining geometric and spatial integrity while streamlining memory usage and computation overhead.
   • This refinement stage revisits initial proposals, enhancing their detail and accuracy, and importantly, adapts to the architecture's memory constraints, allowing efficient feature aggregation and encoding for each 3D proposal.

Experimental Results

The effectiveness of CAGroup3D was empirically validated against benchmarks on ScanNet V2 and SUN RGB-D datasets, showing substantial improvements. Specifically, the framework achieved a notable [email protected] increase of +3.6% on ScanNet V2 and +2.6% on SUN RGB-D, outperforming several state-of-the-art baselines.

Impact and Implications:

• Practical Implications:

The class-aware grouping and sparse RoI-Conv pooling techniques demonstrate potential enhancements for applications in autonomous driving, robotics, and augmented reality by delivering more precise object localization even in complex, densely populated scenes.

• Theoretical Contributions:

The paper advances the approach to object detection by integrating semantic awareness directly into the proposal generation process, encouraging future research to explore the balance between computational efficiency and detection accuracy through class-sensitive strategies.

Future Directions

Given that CAGroup3D primarily addresses inter-category distinctions, an intriguing extension would be exploring intra-category variations, potentially leveraging unsupervised or semi-supervised learning to further enhance localization in mixed or partially labeled datasets. Additionally, further optimization of computational overhead can extend its applicability across varied hardware constraints.

In conclusion, CAGroup3D encapsulates a precise, memory-efficient, and robust framework for 3D object detection, setting a benchmark in incorporating semantic awareness within voxel-based detection systems. This paper serves as a pivotal reference point for ongoing advancements in 3D vision technologies.