[ICCV 23] Ada3D: Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection

Image credit: Unsplash


Voxel-based methods have achieved state-of-the-art performance for 3D object detection in autonomous driving. However, their significant computational and memory costs pose a challenge for their application to resourceconstrained vehicles. One reason for this high resource consumption is the presence of a large number of redundant background points in Lidar point clouds, resulting in spatial redundancy in both 3D voxel and BEV map representations. To address this issue, we propose an adaptive inference framework called Ada3D, which focuses on reducing the spatial redundancy to compress the model’s computational and memory cost. Ada3D adaptively filters the redundant input, guided by a lightweight importance predictor and the unique properties of the Lidar point cloud. Additionally, we maintain the BEV features’ intrinsic sparsity by introducing the Sparsity Preserving Batch Normalization. With Ada3D, we achieve 40% reduction for 3D voxels and decrease the density of 2D BEV feature maps from 100% to 20% without sacrificing accuracy. Ada3D reduces the model computational and memory cost by 5×, and achieves 1.52× / 1.45× end-to-end GPU latency and 1.5× / 4.5× GPU peak memory optimization for the 3D and 2D backbone respectively.

In International Conference of Computer Vision 2023
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.

Tianchen Zhao
Tianchen Zhao
Phd. Student at Tsinghua University

I’m a Phd. student in Tsinghua University, my research focus is Effieicent Deep Learning