# A Point-Based Approach to Efficient LiDAR Multi-Task Perception

Source: [A Point-Based Approach to Efficient LiDAR Multi-Task Perception](<https://www.sota2.com/research/paper/68fa72ee1e209131cea5957c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:37:39.153Z

Authors: Christopher Lang, Alexander Braun, Lars Schillingmann, Abhinav Valada

Published: 2024-04-19T11:24:34.000Z

## Abstract

Multi-task networks can potentially improve performance and computational efficiency compared to single-task networks, facilitating online deployment. However, current multi-task architectures in point cloud perception combine multiple task-specific point cloud representations, each requiring a separate feature encoder and making the network structures bulky and slow. We propose PAttFormer, an efficient multi-task architecture for joint semantic segmentation and object detection in point clouds that only relies on a point-based representation. The network builds on transformer-based feature encoders using neighborhood attention and grid-pooling and a query-based detection decoder using a novel 3D deformable-attention detection head design. Unlike other LiDAR-based multi-task architectures, our proposed PAttFormer does not require separate feature encoders for multiple task-specific point cloud representations, resulting in a network that is 3x smaller and 1.4x faster while achieving competitive performance on the nuScenes and KITTI benchmarks for autonomous driving perception. Our extensive evaluations show substantial gains from multi-task learning, improving LiDAR semantic segmentation by +1.7% in mIou and 3D object detection by +1.7% in mAP on the nuScenes benchmark compared to the single-task models.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.12798>)

[PDF](<https://arxiv.org/pdf/2404.12798.pdf>)