# Co-Fix3D: Enhancing 3D Object Detection with Collaborative Refinement

arXiv: https://arxiv.org/abs/2408.07999 · HTML: https://mlsanity.com/paper/2408.07999/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection](https://mlsanity.com/paper/1908.09492/) (2019)
- [BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework](https://mlsanity.com/paper/2205.13790/) (2022)
- [Unifying Voxel-based Representation with Transformer for 3D Object Detection](https://mlsanity.com/paper/2206.00630/) (2022)
- [Multimodal Virtual Point 3D Detection](https://mlsanity.com/paper/2111.06881/) (2021)
- [DeepInteraction: 3D Object Detection via Modality Interaction](https://mlsanity.com/paper/2208.11112/) (2022)
- [SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection](https://mlsanity.com/paper/2403.05817/) (2024)
- [HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/2310.20234/) (2023)
- [Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations](https://mlsanity.com/paper/2008.08766/) (2020)

## Built on by (most cited first, 2 of 2)

- [CCF: Complementary Collaborative Fusion for Domain Generalized Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2603.23276/) (2026)
- [Co-Fusion4D: Spatio-temporal Collaborative Fusion for Robust 3D Object Detection](https://mlsanity.com/paper/2605.20301/) (2026)

## Abstract (arXiv)

3D object detection in driving scenarios faces the challenge of complex road environments, which can lead to the loss or incompleteness of key features, thereby affecting perception performance. To address this issue, we propose an advanced detection framework called Co-Fix3D. Co-Fix3D integrates Local and Global Enhancement (LGE) modules to refine Bird's Eye View (BEV) features. The LGE module uses Discrete Wavelet Transform (DWT) for pixel-level local optimization and incorporates an attention mechanism for global optimization. To handle varying detection difficulties, we adopt multi-head LGE modules, enabling each module to focus on targets with different levels of detection complexity, thus further enhancing overall perception capability. Experimental results show that on the nuScenes dataset's LiDAR benchmark, Co-Fix3D achieves 69.4% mAP and 73.5% NDS, while on the multimodal benchmark, it achieves 72.3% mAP and 74.7% NDS. The source code is publicly available at hrefhttps://github.com/rubbish001/Co-Fix3dhttps://github.com/rubbish001/Co-Fix3d.