HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds (2310.20234v1)

Abstract: 3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene. Existing high-performance methods typically employ 3D sparse convolutional neural networks with small kernels to extract features. To reduce computational costs, these methods resort to submanifold sparse convolutions, which prevent the information exchange among spatially disconnected features. Some recent approaches have attempted to address this problem by introducing large-kernel convolutions or self-attention mechanisms, but they either achieve limited accuracy improvements or incur excessive computational costs. We propose HEDNet, a hierarchical encoder-decoder network for 3D object detection, which leverages encoder-decoder blocks to capture long-range dependencies among features in the spatial space, particularly for large and distant objects. We conducted extensive experiments on the Waymo Open and nuScenes datasets. HEDNet achieved superior detection accuracy on both datasets than previous state-of-the-art methods with competitive efficiency. The code is available at https://github.com/zhanggang001/HEDNet.

• The paper introduces the SED block that efficiently captures long-range dependencies within sparse CNNs for improved 3D detection.
• The paper incorporates a DED block to augment sparse features toward object centers, enhancing detection robustness.
• The paper demonstrates significant performance gains on Waymo and nuScenes datasets while boosting computational efficiency by up to 50%.

Overview of HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds

The paper presents HEDNet, a hierarchical encoder-decoder network designed to enhance the performance of 3D object detection within point clouds. This research addresses the limitations inherent in existing approaches, particularly focusing on the challenge posed by the sparse distribution of points in 3D detection tasks.

Context and Challenges

3D object detection is critical for autonomous driving systems, yet the task faces significant hurdles due to the inherent sparsity in point cloud data. Existing high-performance methods tend to leverage voxel-based representation strategies, which partition point cloud data into regular grids for processing through sparse CNNs or transformers. These methods encounter trade-offs: submanifold sparse convolutions are efficient but struggle to capture long-range dependencies due to their local connectivity, while regular sparse convolutions introduce high computational costs.

Previous approaches incorporating large-kernel convolutions or self-attention mechanisms have not effectively balanced accuracy improvements with computational efficiency. Thus, this paper proposes the novel concept of a sparse encoder-decoder block (SED block) as a solution to capturing long-range dependencies in a computationally efficient manner.

Technical Contributions

• SED Block: HEDNet’s core innovation, the SED block, adopts an encoder-decoder architecture within sparse CNNs. By employing feature down-sampling and up-sampling, the SED block effectively facilitates information exchange among spatially disconnected regions. This allows the network to capture long-range dependencies without compromising feature sparsity, thereby mitigating computational overhead.
• DED Block: Recognizing the importance of object centers in high-performance 3D detection, the paper introduces the dense encoder-decoder block (DED block). The DED block augments sparse features by expanding them towards object centers, enhancing robustness in predictive precision, particularly for large and distant objects.

HEDNet integrates these blocks into a cohesive hierarchy, optimizing feature representation from the voxel level through higher-level abstractions within both the sparse and dense domains.

Empirical Validation

HEDNet was empirically validated on the challenging Waymo Open and nuScenes datasets, demonstrating superior performance metrics over state-of-the-art models. Specifically, HEDNet achieved a 75.0% L2 mAPH on the Waymo Open test set and a 72.0% NDS on the nuScenes test set. Notably, HEDNet was able to achieve these benchmarks while maintaining competitive efficiency, showcasing a 50% increase in speed over the transformer-based DSVT model, achieving an L2 mAPH gain of 1.3%.

Implications and Future Directions

HEDNet’s architecture establishes a new paradigm for optimizing 3D object detection in point clouds by addressing both long-range dependency challenges and computational efficiency. This positions HEDNet as a significant methodological advancement suitable for real-world applications, particularly in autonomous driving.

The adoption of encoder-decoder structures within sparse CNN frameworks may stimulate further exploration into optimizing similar architectures for other dense prediction tasks. Future developments could explore extensions into multi-modal fusion and dynamic adaptation, enhancing HEDNet's applicability across a diverse range of environmental conditions and object classes.

In conclusion, the introduction of HEDNet marks a pivotal contribution to the landscape of 3D object detection, balancing accuracy and computational efficiency through innovative architectural design. This research not only enhances current detection capabilities but also paves the way for the development of more sophisticated methods in machine perception.