# 3D Object Detection Using Scale Invariant and Feature Reweighting Networks

arXiv: https://arxiv.org/abs/1901.02237 · HTML: https://mlsanity.com/paper/1901.02237/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2019.

## Built on by (most cited first, 23 of 23)

- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [Focal Sparse Convolutional Networks for 3D Object Detection](https://mlsanity.com/paper/2204.12463/) (2022)
- [Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion](https://mlsanity.com/paper/2203.09780/) (2022)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://mlsanity.com/paper/2106.12735/) (2021)
- [EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2112.11088/) (2021)
- [VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion](https://mlsanity.com/paper/2111.14382/) (2021)
- [CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection](https://mlsanity.com/paper/2204.00325/) (2022)
- [OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2403.01644/) (2024)
- [HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection](https://mlsanity.com/paper/2206.15157/) (2022)
- [3D Cascade RCNN: High Quality Object Detection in Point Clouds](https://mlsanity.com/paper/2211.08248/) (2022)
- [CLFT: Camera-LiDAR Fusion Transformer for Semantic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2404.17793/) (2024)
- [FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection](https://mlsanity.com/paper/2209.07419/) (2022)
- [SparseDet: Towards End-to-End 3D Object Detection](https://mlsanity.com/paper/2206.00960/) (2022)
- [Multi-level and multi-modal feature fusion for accurate 3D object detection in Connected and Automated Vehicles](https://mlsanity.com/paper/2212.07560/) (2022)
- [Cognitive TransFuser: Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint Prediction](https://mlsanity.com/paper/2308.02126/) (2023)
- [Feature Reweighting for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2308.02515/) (2023)
- [Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey](https://mlsanity.com/paper/2311.06043/) (2023)
- [A re-calibration method for object detection with multi-modal alignment bias in autonomous driving](https://mlsanity.com/paper/2405.16848/) (2024)
- [3D-FFS: Faster 3D object detection with Focused Frustum Search in sensor fusion based networks](https://mlsanity.com/paper/2103.08294/) (2021)
- [ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection](https://mlsanity.com/paper/2211.09518/) (2022)
- [PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion](https://mlsanity.com/paper/2212.06244/) (2022)
- [PTA-Det: Point Transformer Associating Point cloud and Image for 3D Object Detection](https://mlsanity.com/paper/2301.07301/) (2023)

## Abstract (arXiv)

3D object detection plays an important role in a large number of real-world applications. It requires us to estimate the localizations and the orientations of 3D objects in real scenes. In this paper, we present a new network architecture which focuses on utilizing the front view images and frustum point clouds to generate 3D detection results. On the one hand, a PointSIFT module is utilized to improve the performance of 3D segmentation. It can capture the information from different orientations in space and the robustness to different scale shapes. On the other hand, our network obtains the useful features and suppresses the features with less information by a SENet module. This module reweights channel features and estimates the 3D bounding boxes more effectively. Our method is evaluated on both KITTI dataset for outdoor scenes and SUN-RGBD dataset for indoor scenes. The experimental results illustrate that our method achieves better performance than the state-of-the-art methods especially when point clouds are highly sparse.