Reproduce “HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds” on OpenResearch
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About this paper
Gang Zhang, Junnan Chen, Guohuan Gao, Jianmin Li, Xiaolin Hu
HEDNet, developed by Tsinghua University, introduces a hierarchical encoder-decoder network for 3D object detection in point clouds. It achieves state-of-the-art accuracy on Waymo Open and nuScenes datasets, particularly for large and distant objects, while demonstrating 50% faster inference than previous methods.
Key insights
- Encoder-decoder structures can be effectively adapted to sparse 3D convolutional networks to capture long-range dependencies efficiently without increasing feature map density.
- Maintaining sparsity during up-sampling within a sparse encoder-decoder (SED block) is crucial for building efficient, stacked 3D backbones.
- Applying a dense encoder-decoder (DED block) on 2D Bird's Eye View (BEV) features effectively addresses sparse "empty holes" around object centers, improving detection for large objects.
Summary by alphaXiv.
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