A Unified Query-based Paradigm for Point Cloud Understanding (2203.01252v3)

Abstract: 3D point cloud understanding is an important component in autonomous driving and robotics. In this paper, we present a novel Embedding-Querying paradigm (EQ- Paradigm) for 3D understanding tasks including detection, segmentation, and classification. EQ-Paradigm is a unified paradigm that enables the combination of any existing 3D backbone architectures with different task heads. Under the EQ-Paradigm, the input is firstly encoded in the embedding stage with an arbitrary feature extraction architecture, which is independent of tasks and heads. Then, the querying stage enables the encoded features to be applicable for diverse task heads. This is achieved by introducing an intermediate representation, i.e., Q-representation, in the querying stage to serve as a bridge between the embedding stage and task heads. We design a novel Q- Net as the querying stage network. Extensive experimental results on various 3D tasks, including object detection, semantic segmentation and shape classification, show that EQ-Paradigm in tandem with Q-Net is a general and effective pipeline, which enables a flexible collaboration of backbones and heads, and further boosts the performance of the state-of-the-art methods. Codes and models are available at https://github.com/dvlab-research/DeepVision3D.

• The paper presents a novel EQ-Paradigm that unifies feature extraction and task-specific queries, streamlining 3D point cloud understanding.
• The introduction of Q-Net, a transformer-based network, refines intermediate representations to achieve improved mIoU and mAP on standard benchmarks.
• The framework supports flexible integration of voxel-based and point-based backbones, enabling effective adaptation for segmentation, detection, and classification tasks.

Analyzing the Unified Query-based Paradigm for Point Cloud Understanding

The paper presents a novel framework, the Embedding-Querying paradigm (EQ-Paradigm), designed to enhance the understanding of 3D point clouds across various tasks—including detection, segmentation, and classification. This paradigm offers a unified approach that integrates existing 3D backbone architectures with various task-specific heads, leveraging a flexible and adaptable structure. The core innovation of the EQ-Paradigm is the introduction of the Q-Net, a querying stage network, which facilitates the generation of task-specific representations from encoded features.

EQ-Paradigm Overview and Implementation

The EQ-Paradigm consists of three stages: the Embedding stage, the Querying stage, and the task-specific Head.

• Embedding Stage: This stage is populated by any feature extraction architecture, including both voxel-based and point-based networks. The flexibility allows this stage to be task-independent and agnostic to the head design, thereby extracting support features and support points irrespective of the downstream application.
• Querying Stage: At the heart of the EQ-Paradigm is the querying stage, which bridges the embedding stage and task heads using the Q-Net. The key innovation here is the Q-Representation, an intermediate feature representation that can be generated for any 3D position within the input scene, thus enabling novel combinations of backbone architectures and task heads.
• Task Head: Once the Q-Representation is generated, it passes to a task head, designed to convert these features into specific predictions like object classification labels or segmentation masks.

Q-Net: Architecture and Contributions

Q-Net, central to the querying stage, is a transformer-based network that iteratively refines query features derived from support points and features. The choice of the transformer architecture is pivotal as it brings a global receptive field and leverages position encoding beneficial for spatial flexibility.

Key components of the Q-Net include:

• Q-Block: Comprising multiple layers of Q-Blocks, Q-Net updates query and support features iteratively. Each block consists of a Q-Encoder layer to update support features and a Q-Decoder layer to refine query features.
• Hierarchical Q-Net: For tasks like semantic segmentation, a hierarchy of Q-Nets aggregates multi-level features, enhancing the model's ability to capture both global structure and fine-grained details across varying point cloud scales.
• Local Attention Mechanism: To handle large input sizes, a focused attention mechanism is utilized, limiting computations to a local neighborhood of each query point, thus optimizing memory usage and computational efficiency.

Empirical Evaluation and Results

The research extensively benchmarks the EQ-Paradigm across different tasks: semantic segmentation, indoor and outdoor object detection, and shape classification. The empirical studies showcase consistent performance improvements across diverse datasets like ScanNetV2, S3DIS, KITTI, and ModelNet40. For instance, EQ-Paradigm-enhanced models consistently surpass their counterparts on metrics such as mean Intersection-over-Union (mIoU) for segmentation and mean Average Precision (mAP) for object detection.

Implications and Future Work

From a practical standpoint, the EQ-Paradigm provides significant flexibility in the design of 3D point cloud understanding models, allowing practitioners to tailor architectures and combine strategies from voxel-based and point-based frameworks. Theoretically, this paradigm proposes a more unified approach to handling diverse 3D tasks, potentially facilitating the development of generalized models that can seamlessly switch between task objectives with minimal reconfiguration.

Future research could explore the integration of more advanced feature extraction backbones within the EQ-Paradigm, as well as enhancements to the querying strategy that may further improve the spatial representations of 3D scenes.

In conclusion, the proposed EQ-Paradigm with Q-Net offers a versatile and powerful approach to advancing the capabilities of point cloud understanding methodologies, equipping researchers and practitioners with a robust framework for tackling complex 3D vision tasks.