mlsanityData to 14 Oct 2026

XD-RCDepth: Lightweight Radar-Camera Depth Estimation with Explainability-Aligned and Distribution-Aware Distillation

arXiv 2510.13565 ↗First posted 15 Oct 2025cs.CVcited by 1

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Abstract (arXiv)

Depth estimation remains central to autonomous driving, and radar-camera fusion offers robustness in adverse conditions by providing complementary geometric cues. In this paper, we present XD-RCDepth, a lightweight architecture that reduces the parameters by 29.7% relative to the state-of-the-art lightweight baseline while maintaining comparable accuracy. To preserve performance under compression and enhance interpretability, we introduce two knowledge-distillation strategies: an explainability-aligned distillation that transfers the teacher's saliency structure to the student, and a depth-distribution distillation that recasts depth regression as soft classification over discretized bins. Together, these components reduce the MAE compared with direct training with 7.97% and deliver competitive accuracy with real-time efficiency on nuScenes and ZJU-4DRadarCam datasets. Code: https://github.com/harborsarah/XD_RCDepth

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 38 of its 42 references that are in the map. First posted on arXiv 15 Oct 2025.

Builds on

38 of 42 references.

References · 38

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  6. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  7. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  8. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  9. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  10. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  11. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  12. 2014FitNets: Hints for Thin Deep Netscited by 1,653
26 more
  1. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,319
  2. 2016Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfercited by 1,077
  3. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 939
  4. 2019Attention is not Explanationcited by 929
  5. 2019Relational Knowledge Distillationcited by 889
  6. 2020Quantifying Attention Flow in Transformerscited by 775
  7. 2019Contrastive Representation Distillationcited by 748
  8. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
  9. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 382
  10. 2024UniDepth: Universal Monocular Metric Depth Estimationcited by 382
  11. 2019Structured Knowledge Distillation for Dense Predictioncited by 279
  12. 2019Distilling Object Detectors with Fine-grained Feature Imitationcited by 189
  13. 2022P3Depth: Monocular Depth Estimation with a Piecewise Planarity Priorcited by 107
  14. 2024Bilateral Propagation Network for Depth Completioncited by 60
  15. 2023IEBins: Iterative Elastic Bins for Monocular Depth Estimationcited by 54
  16. 2021Radar-Camera Pixel Depth Association for Depth Completioncited by 50
  17. 2023LRRU: Long-short Range Recurrent Updating Networks for Depth Completioncited by 49
  18. 2024RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scalecited by 20
  19. 2024Good Teachers Explain: Explanation-Enhanced Knowledge Distillationcited by 14
  20. 2021Learning Interpretation with Explainable Knowledge Distillationcited by 13
  21. 2024CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimationcited by 12
  22. 2024GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsamplingcited by 7
  23. 2022Utilizing Explainable AI for improving the Performance of Neural Networkscited by 5
  24. 2024LiRCDepth: Lightweight Radar-Camera Depth Estimation via Knowledge Distillation and Uncertainty Guidancecited by 5
  25. 20254D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challengescited by 4
  26. 2025TRIDE: A Text-assisted Radar-Image weather-aware fusion network for Depth Estimationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026RbFT-Net: Rectify-Before-Fuse Temporal Radar Anchors for 4D Radar-Camera Depth Completioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Boosting Robustness for All-Weather Self-Supervised Depth Estimation in Autonomous Driving
  2. 2026Selection, Not Fusion: Radar-Modulated State Space Models for Radar-Camera Depth Estimation
  3. 2025IMKD: Intensity-Aware Multi-Level Knowledge Distillation for Camera-Radar Fusion
  4. 2026ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device
  5. 2024CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation
  6. 2022Dense Depth Distillation with Out-of-Distribution Simulated Images
  7. 2021Boosting Light-Weight Depth Estimation Via Knowledge Distillation
  8. 2025Always Clear Depth: Robust Monocular Depth Estimation under Adverse Weather