HeightFormer: Learning Height Prediction in Voxel Features for Roadside Vision Centric 3D Object Detection via Transformer
Journal:
arXiv
Published Date:
Mar 13, 2025
Abstract
Roadside vision centric 3D object detection has received increasing attention
in recent years. It expands the perception range of autonomous vehicles,
enhances the road safety. Previous methods focused on predicting per-pixel
height rather than depth, making significant gains in roadside visual
perception. While it is limited by the perspective property of near-large and
far-small on image features, making it difficult for network to understand real
dimension of objects in the 3D world. BEV features and voxel features present
the real distribution of objects in 3D world compared to the image features.
However, BEV features tend to lose details due to the lack of explicit height
information, and voxel features are computationally expensive. Inspired by this
insight, an efficient framework learning height prediction in voxel features
via transformer is proposed, dubbed HeightFormer. It groups the voxel features
into local height sequences, and utilize attention mechanism to obtain height
distribution prediction. Subsequently, the local height sequences are
reassembled to generate accurate 3D features. The proposed method is applied to
two large-scale roadside benchmarks, DAIR-V2X-I and Rope3D. Extensive
experiments are performed and the HeightFormer outperforms the state-of-the-art
methods in roadside vision centric 3D object detection task.
