# PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss

arXiv: https://arxiv.org/abs/2103.07362 · HTML: https://mlsanity.com/paper/2103.07362/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 48 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 30 of 32)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Sparsity Invariant CNNs](https://mlsanity.com/paper/1708.06500/) (2017)
- [How Much Position Information Do Convolutional Neural Networks Encode?](https://mlsanity.com/paper/2001.08248/) (2020)
- [Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras](https://mlsanity.com/paper/1904.04998/) (2019)
- [Semantically-Guided Representation Learning for Self-Supervised Monocular Depth](https://mlsanity.com/paper/2002.12319/) (2020)
- [Self-Supervised Monocular Depth Hints](https://mlsanity.com/paper/1909.09051/) (2019)
- [CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth](https://mlsanity.com/paper/1904.02028/) (2019)
- [The Edge of Depth: Explicit Constraints between Segmentation and Depth](https://mlsanity.com/paper/2004.00171/) (2020)
- [Learning Monocular Depth by Distilling Cross-domain Stereo Networks](https://mlsanity.com/paper/1808.06586/) (2018)
- [SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation](https://mlsanity.com/paper/1810.01849/) (2018)
- [Learning monocular depth estimation infusing traditional stereo knowledge](https://mlsanity.com/paper/1904.04144/) (2019)
- [Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes](https://mlsanity.com/paper/2008.03633/) (2020)
- [Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation](https://mlsanity.com/paper/1903.04202/) (2019)
- [Single Image Depth Estimation Trained via Depth from Defocus Cues](https://mlsanity.com/paper/2001.05036/) (2020)
- [Single View Stereo Matching](https://mlsanity.com/paper/1803.02612/) (2018)
- [Learning monocular depth estimation with unsupervised trinocular assumptions](https://mlsanity.com/paper/1808.01606/) (2018)
- [Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation](https://mlsanity.com/paper/2008.07130/) (2020)
- [Unsupervised High-Resolution Depth Learning From Videos With Dual Networks](https://mlsanity.com/paper/1910.08897/) (2019)
- [Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding](https://mlsanity.com/paper/1806.10556/) (2018)

## Built on by (most cited first, 30 of 31)

- [P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior](https://mlsanity.com/paper/2204.02091/) (2022)
- [PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes](https://mlsanity.com/paper/2210.01612/) (2022)
- [Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV](https://mlsanity.com/paper/2307.10713/) (2023)
- [SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation](https://mlsanity.com/paper/2309.00526/) (2023)
- [Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation](https://mlsanity.com/paper/2503.15905/) (2025)
- [Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes](https://mlsanity.com/paper/2310.18887/) (2023)
- [Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter](https://mlsanity.com/paper/2208.01489/) (2022)
- [Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2209.07088/) (2022)
- [The Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2211.12174/) (2022)
- [GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes](https://mlsanity.com/paper/2309.16019/) (2023)
- [From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior](https://mlsanity.com/paper/2312.10118/) (2023)
- [The Second Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2304.07051/) (2023)
- [The Third Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2404.16831/) (2024)
- [Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation](https://mlsanity.com/paper/2309.00933/) (2023)
- [SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2404.12501/) (2024)
- [The Fourth Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2504.17787/) (2025)
- [Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTV](https://mlsanity.com/paper/2403.01569/) (2024)
- [Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2203.10925/) (2022)
- [Globally Consistent Video Depth and Pose Estimation with Efficient Test-Time Training](https://mlsanity.com/paper/2208.02709/) (2022)
- [Single-View View Synthesis with Self-Rectified Pseudo-Stereo](https://mlsanity.com/paper/2304.09527/) (2023)
- [EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity](https://mlsanity.com/paper/2309.01296/) (2023)
- [Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2309.05254/) (2023)
- [MambaDepth: Enhancing Long-range Dependency for Self-Supervised Fine-Structured Monocular Depth Estimation](https://mlsanity.com/paper/2406.04532/) (2024)
- [Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videos](https://mlsanity.com/paper/2205.08851/) (2022)
- [Novel View Synthesis with View-Dependent Effects from a Single Image](https://mlsanity.com/paper/2312.08071/) (2023)
- [SoK: Vehicle Orientation Representations for Deep Rotation Estimation](https://mlsanity.com/paper/2112.04421/) (2021)
- [HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decoder](https://mlsanity.com/paper/2305.18706/) (2023)
- [Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation](https://mlsanity.com/paper/2307.09929/) (2023)
- [Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2210.03952/) (2022)
- [Rebenchmarking Unsupervised Monocular 3D Occupancy Prediction](https://mlsanity.com/paper/2602.06488/) (2026)

## Abstract (arXiv)

In this paper, we propose a self-supervised single-view pixel-level accurate depth estimation network, called PLADE-Net. The PLADE-Net is the first work that shows unprecedented accuracy levels, exceeding 95% in terms of the δ1 metric on the challenging KITTI dataset. Our PLADE-Net is based on a new network architecture with neural positional encoding and a novel loss function that borrows from the closed-form solution of the matting Laplacian to learn pixel-level accurate depth estimation from stereo images. Neural positional encoding allows our PLADE-Net to obtain more consistent depth estimates by letting the network reason about location-specific image properties such as lens and projection distortions. Our novel distilled matting Laplacian loss allows our network to predict sharp depths at object boundaries and more consistent depths in highly homogeneous regions. Our proposed method outperforms all previous self-supervised single-view depth estimation methods by a large margin on the challenging KITTI dataset, with unprecedented levels of accuracy. Furthermore, our PLADE-Net, naively extended for stereo inputs, outperforms the most recent self-supervised stereo methods, even without any advanced blocks like 1D correlations, 3D convolutions, or spatial pyramid pooling. We present extensive ablation studies and experiments that support our method's effectiveness on the KITTI, CityScapes, and Make3D datasets.