# Efficient Multimodal 3D Object Detector via Instance-Level Contrastive Distillation

arXiv: https://arxiv.org/abs/2503.12914 · HTML: https://mlsanity.com/paper/2503.12914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model](https://mlsanity.com/paper/2408.11039/) (2024)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking](https://mlsanity.com/paper/2303.11301/) (2023)
- [EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection](https://mlsanity.com/paper/2007.08856/) (2020)
- [Demystify Mamba in Vision: A Linear Attention Perspective](https://mlsanity.com/paper/2405.16605/) (2024)
- [SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection](https://mlsanity.com/paper/2304.14340/) (2023)
- [Virtual Sparse Convolution for Multimodal 3D Object Detection](https://mlsanity.com/paper/2303.02314/) (2023)
- [LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion](https://mlsanity.com/paper/2303.03595/) (2023)
- [BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2211.09386/) (2022)
- [IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection](https://mlsanity.com/paper/2403.15241/) (2024)
- [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)
- [UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View](https://mlsanity.com/paper/2303.15083/) (2023)
- [TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning](https://mlsanity.com/paper/2212.13979/) (2022)
- [BirdNet: a 3D Object Detection Framework from LiDAR information](https://mlsanity.com/paper/1805.01195/) (2018)
- [CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection](https://mlsanity.com/paper/2204.00325/) (2022)

## Abstract (arXiv)

Multimodal 3D object detectors leverage the strengths of both geometry-aware LiDAR point clouds and semantically rich RGB images to enhance detection performance. However, the inherent heterogeneity between these modalities, including unbalanced convergence and modal misalignment, poses significant challenges. Meanwhile, the large size of the detection-oriented feature also constrains existing fusion strategies to capture long-range dependencies for the 3D detection tasks. In this work, we introduce a fast yet effective multimodal 3D object detector, incorporating our proposed Instance-level Contrastive Distillation (ICD) framework and Cross Linear Attention Fusion Module (CLFM). ICD aligns instance-level image features with LiDAR representations through object-aware contrastive distillation, ensuring fine-grained cross-modal consistency. Meanwhile, CLFM presents an efficient and scalable fusion strategy that enhances cross-modal global interactions within sizable multimodal BEV features. Extensive experiments on the KITTI and nuScenes 3D object detection benchmarks demonstrate the effectiveness of our methods. Notably, our 3D object detector outperforms state-of-the-art (SOTA) methods while achieving superior efficiency. The implementation of our method has been released as open-source at: https://github.com/nubot-nudt/ICD-Fusion.