# Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework

arXiv: https://arxiv.org/abs/2407.05769 · HTML: https://mlsanity.com/paper/2407.05769/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 48 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 30 of 36)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [3DSSD: Point-based 3D Single Stage Object Detector](https://mlsanity.com/paper/2002.10187/) (2020)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://mlsanity.com/paper/1907.03670/) (2019)
- [Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection](https://mlsanity.com/paper/1908.09492/) (2019)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [Self-Supervised Pretraining of 3D Features on any Point-Cloud](https://mlsanity.com/paper/2101.02691/) (2021)
- [End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/1910.06528/) (2019)
- [EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection](https://mlsanity.com/paper/2007.08856/) (2020)
- [Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds](https://mlsanity.com/paper/2203.11139/) (2022)
- [H3DNet: 3D Object Detection Using Hybrid Geometric Primitives](https://mlsanity.com/paper/2006.05682/) (2020)
- [SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud](https://mlsanity.com/paper/2104.09804/) (2021)
- [CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud](https://mlsanity.com/paper/2012.03015/) (2020)
- [Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection](https://mlsanity.com/paper/2109.02499/) (2021)
- [Point Density-Aware Voxels for LiDAR 3D Object Detection](https://mlsanity.com/paper/2203.05662/) (2022)
- [SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection](https://mlsanity.com/paper/2201.01976/) (2022)
- [Transformation-Equivariant 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2211.11962/) (2022)
- [EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2112.11088/) (2021)
- [VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention](https://mlsanity.com/paper/2203.09704/) (2022)
- [HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection](https://mlsanity.com/paper/2104.00902/) (2021)
- [ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2207.12654/) (2022)
- [SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds](https://mlsanity.com/paper/2006.04043/) (2020)
- [From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection](https://mlsanity.com/paper/2107.14391/) (2021)
- [GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation](https://mlsanity.com/paper/2207.02466/) (2022)
- [BADet: Boundary-Aware 3D Object Detection from Point Clouds](https://mlsanity.com/paper/2104.10330/) (2021)
- [Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion](https://mlsanity.com/paper/2011.01404/) (2020)

## Built on by (most cited first, 1 of 1)

- [GVSynergy-Det: Synergistic Gaussian-Voxel Representations for Multi-View 3D Object Detection](https://mlsanity.com/paper/2512.23176/) (2025)

## Abstract (arXiv)

In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds, providing reliable geometric information. However, traditional sampling methods of preprocessing often ignore semantic features, leading to detail loss and ground point interference in 3D object detection. To address this, we propose a multi-branch two-stage 3D object detection framework using a Semantic-aware Multi-branch Sampling (SMS) module and multi-view consistency constraints. The SMS module includes random sampling, Density Equalization Sampling (DES) for enhancing distant objects, and Ground Abandonment Sampling (GAS) to focus on non-ground points. The sampled multi-view points are processed through a Consistent KeyPoint Selection (CKPS) module to generate consistent keypoint masks for efficient proposal sampling. The first-stage detector uses multi-branch parallel learning with multi-view consistency loss for feature aggregation, while the second-stage detector fuses multi-view data through a Multi-View Fusion Pooling (MVFP) module to precisely predict 3D objects. The experimental results on the KITTI dataset and Waymo Open Dataset show that our method achieves excellent detection performance improvement for a variety of backbones, especially for low-performance backbones with the simple network structures.