mlsanityData to 7 Oct 2026

Depth Estimation Algorithm Based on Transformer-Encoder and Feature Fusion

arXiv 2403.01370 ↗First posted 3 Mar 2024cs.CVcited by 0

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Abstract (arXiv)

This research presents a novel depth estimation algorithm based on a Transformer-encoder architecture, tailored for the NYU and KITTI Depth Dataset. This research adopts a transformer model, initially renowned for its success in natural language processing, to capture intricate spatial relationships in visual data for depth estimation tasks. A significant innovation of the research is the integration of a composite loss function that combines Structural Similarity Index Measure (SSIM) with Mean Squared Error (MSE). This combined loss function is designed to ensure the structural integrity of the predicted depth maps relative to the original images (via SSIM) while minimizing pixel-wise estimation errors (via MSE). This research approach addresses the challenges of over-smoothing often seen in MSE-based losses and enhances the model's ability to predict depth maps that are not only accurate but also maintain structural coherence with the input images. Through rigorous training and evaluation using the NYU Depth Dataset, the model demonstrates superior performance, marking a significant advancement in single-image depth estimation, particularly in complex indoor and traffic environments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 3 Mar 2024.

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References · 16

  1. 2020AdaBins: Depth Estimation using Adaptive Binscited by 596
  2. 2022Unifying Flow, Stereo and Depth Estimationcited by 270
  3. 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 233
  4. 2018Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstructioncited by 159
  5. 2022NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimationcited by 151
  6. 2019Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentationcited by 141
  7. 2020Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volumecited by 116
  8. 2022DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimationcited by 116
  9. 2017Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimationcited by 70
  10. 2021Single Image Depth Estimation: An Overviewcited by 35
  11. 2022Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusioncited by 20
  12. 2022Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsicscited by 19
4 more
  1. 2021GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Networkcited by 12
  2. 2022A CNN Based Approach for the Point-Light Photometric Stereo Problemcited by 10
  3. 2022Depth Estimation with Simplified Transformercited by 8
  4. 2022Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimationcited by 1

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