# RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth

arXiv: https://arxiv.org/abs/2108.00616 · HTML: https://mlsanity.com/paper/2108.00616/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016)
- [Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection](https://mlsanity.com/paper/1909.01955/) (2019)
- [Learning to predict crisp boundaries](https://mlsanity.com/paper/1807.10097/) (2018)
- [Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection](https://mlsanity.com/paper/1901.06340/) (2019)
- [Bi-Directional Cascade Network for Perceptual Edge Detection](https://mlsanity.com/paper/1902.10903/) (2019)
- [Designing Deep Networks for Surface Normal Estimation](https://mlsanity.com/paper/1411.4958/) (2014)
- [Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts](https://mlsanity.com/paper/2004.07684/) (2020)
- [DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection](https://mlsanity.com/paper/1412.1123/) (2014)
- [Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields](https://mlsanity.com/paper/2002.12730/) (2020)
- [CASENet: Deep Category-Aware Semantic Edge Detection](https://mlsanity.com/paper/1705.09759/) (2017)
- [Pushing the Boundaries of Boundary Detection using Deep Learning](https://mlsanity.com/paper/1511.07386/) (2015)
- [Object Contour Detection with a Fully Convolutional Encoder-Decoder Network](https://mlsanity.com/paper/1603.04530/) (2016)
- [Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations](https://mlsanity.com/paper/1904.07934/) (2019)
- [Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction](https://mlsanity.com/paper/1801.00524/) (2018)
- [High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision](https://mlsanity.com/paper/1504.06201/) (2015)
- [Simultaneous Edge Alignment and Learning](https://mlsanity.com/paper/1808.01992/) (2018)
- [Dynamic Feature Fusion for Semantic Edge Detection](https://mlsanity.com/paper/1902.09104/) (2019)
- [Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoning](https://mlsanity.com/paper/1908.05898/) (2019)
- [Object Contour and Edge Detection with RefineContourNet](https://mlsanity.com/paper/1904.13353/) (2019)
- [DOOBNet: Deep Object Occlusion Boundary Detection from an Image](https://mlsanity.com/paper/1806.03772/) (2018)
- [DOC: Deep OCclusion Estimation From a Single Image](https://mlsanity.com/paper/1511.06457/) (2015)

## Built on by (most cited first, 30 of 33)

- [EDTER: Edge Detection with Transformer](https://mlsanity.com/paper/2203.08566/) (2022)
- [Tiny and Efficient Model for the Edge Detection Generalization](https://mlsanity.com/paper/2308.06468/) (2023)
- [The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector](https://mlsanity.com/paper/2303.11828/) (2023)
- [RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses](https://mlsanity.com/paper/2403.01795/) (2024)
- [SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures](https://mlsanity.com/paper/2503.01113/) (2025)
- [Vision Transformer Adapters for Generalizable Multitask Learning](https://mlsanity.com/paper/2308.12372/) (2023)
- [SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events](https://mlsanity.com/paper/2309.12842/) (2023)
- [3D Neural Edge Reconstruction](https://mlsanity.com/paper/2405.19295/) (2024)
- [MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning](https://mlsanity.com/paper/2308.02237/) (2023)
- [Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentation](https://mlsanity.com/paper/2408.12815/) (2024)
- [End-to-End Instance Edge Detection](https://mlsanity.com/paper/2204.02898/) (2022)
- [Boosting Edge Detection with Pixel-wise Feature Selection: The Extractor-Selector Paradigm](https://mlsanity.com/paper/2501.02534/) (2025)
- [MixerCSeg: An Efficient Mixer Architecture for Crack Segmentation via Decoupled Mamba Attention](https://mlsanity.com/paper/2603.01361/) (2026)
- [SuperEdge: Towards a Generalization Model for Self-Supervised Edge Detection](https://mlsanity.com/paper/2401.02313/) (2024)
- [A Comprehensive Study for Edge Detection: Ideal-Prior Guidance Learning, High-Precision Architecture, and Better Distinguishability Evaluation Benchmark](https://mlsanity.com/paper/2407.19992/) (2024)
- [Interactive Occlusion Boundary Estimation through Exploitation of Synthetic Data](https://mlsanity.com/paper/2408.15038/) (2024)
- [Symmetrization Weighted Binary Cross-Entropy: Modeling Perceptual Asymmetry for Human-Consistent Neural Edge Detection](https://mlsanity.com/paper/2501.13365/) (2025)
- [BeltCrack: the First Sequential-image Industrial Conveyor Belt Crack Detection Dataset and Its Baseline with Triple-domain Feature Learning](https://mlsanity.com/paper/2506.17892/) (2025)
- [Dunhuang murals contour generation network based on convolution and self-attention fusion](https://mlsanity.com/paper/2212.00935/) (2022)
- [Multi-Scale Tensorial Summation and Dimensional Reduction Guided Neural Network for Edge Detection](https://mlsanity.com/paper/2504.15770/) (2025)
- [Occlusion Boundary and Depth: Mutual Enhancement via Multi-Task Learning](https://mlsanity.com/paper/2505.21231/) (2025)
- [Binarization-Aware Adjuster for Discrete Decision Learning with an Application to Edge Detection](https://mlsanity.com/paper/2506.12460/) (2025)
- [MatchED: Crisp Edge Detection Using End-to-End, Matching-based Supervision](https://mlsanity.com/paper/2602.20689/) (2026)
- [Rethinking Efficient Crack Segmentation with Task-Aligned Structural-Directional Modeling](https://mlsanity.com/paper/2605.31048/) (2026)
- [ECT: Fine-grained Edge Detection with Learned Cause Tokens](https://mlsanity.com/paper/2308.03092/) (2023)
- [UHNet: An Ultra-Lightweight and High-Speed Edge Detection Network](https://mlsanity.com/paper/2408.04258/) (2024)
- [Training-free Quantum-Inspired Image Edge Extraction Method](https://mlsanity.com/paper/2501.18929/) (2025)
- [Edge-Boundary-Texture Loss: A Tri-Class Generalization of Weighted Binary Cross-Entropy for Enhanced Edge Detection](https://mlsanity.com/paper/2507.06569/) (2025)
- [Fluxamba: Topology-Aware Anisotropic State Space Models for Geological Lineament Segmentation in Multi-Source Remote Sensing](https://mlsanity.com/paper/2601.17288/) (2026)
- [Bridging the Geometry Mismatch: Frequency-Aware Anisotropic Serialization for Thin-Structure SSMs](https://mlsanity.com/paper/2603.28503/) (2026)

## Abstract (arXiv)

As a fundamental building block in computer vision, edges can be categorised into four types according to the discontinuity in surface-Reflectance, Illumination, surface-Normal or Depth. While great progress has been made in detecting generic or individual types of edges, it remains under-explored to comprehensively study all four edge types together. In this paper, we propose a novel neural network solution, RINDNet, to jointly detect all four types of edges. Taking into consideration the distinct attributes of each type of edges and the relationship between them, RINDNet learns effective representations for each of them and works in three stages. In stage I, RINDNet uses a common backbone to extract features shared by all edges. Then in stage II it branches to prepare discriminative features for each edge type by the corresponding decoder. In stage III, an independent decision head for each type aggregates the features from previous stages to predict the initial results. Additionally, an attention module learns attention maps for all types to capture the underlying relations between them, and these maps are combined with initial results to generate the final edge detection results. For training and evaluation, we construct the first public benchmark, BSDS-RIND, with all four types of edges carefully annotated. In our experiments, RINDNet yields promising results in comparison with state-of-the-art methods. Additional analysis is presented in supplementary material.