- DynaDepth [64]: A monocular model that incorporates the motion dynamics data from an inertial measurement unit (IMU) to improve the accuracy and robustness of the depth estimation in challenging conditions. The proposed method is able to estimate the scale of the scene, which is a major challenge in monocular depth estimation, and is also more robust to illumination changes and dynamic scenes. The code is accessible at: https://github.com/SenZHANG-GitHub/ekf-imu-depth.
- RA-Depth [42]: A monocular model that can adaptively adjust the output resolution based on the input image resolution. This is achieved through a two-stage architecture that first generates a coarse depth map and then refines it to the desired output resolution using a depth super-resolution network. The code is accessible at: https://github.com/hmhexu/RA-Depth.
- TriDepth [6]: A monocular model with a new depth estimation loss function that incorporates a scale-invariant gradient term. This allows the model to learn to predict sharper edges and finer details in the depth map, which is critical for accurate depth estimation. The code is accessible at: https://github.com/xingyuuchen/tri-depth.
- Lite-Mono [63]: A monocular model with a lightweight convolution backbone and transformer architecture for self-supervised monocular depth estimation. The authors propose a hybrid approach that combines the strengths of both CNNs and transformers to achieve state-of-the-art performance on several benchmarks while using significantly fewer parameters compared to existing methods. The code is accessible at: https://github.com/noahzn/Lite-Mono.
- BTS [31]: A monocular model involves detecting and utilizing local planar structures in the image at multiple scales to improve depth estimation accuracy. Specifically, the authors propose a novel deep neural network architecture that incorporates a local planar guidance module at each scale of a feature pyramid. This module predicts a set of local planar patches in the image and uses them to provide guidance for the depth estimation module. The code is accessible at: https://github.com/cleinc/bts.
- AdaBins [1]: A monocular model with an adaptive binning scheme that dynamically adjusts the bin sizes of the network to better capture the distribution of depth values in the scene. In traditional binning schemes, fixed bin sizes are used, which can lead to under or over-representation of certain depth values. AdaBins overcomes this limitation by adapting the bin sizes based on the distribution of depth values in the input image. The code is accessible at: https://github.com/shariqfarooq123/AdaBins.
- DPT [43]: A monocular model that leverages Vision Transformers in place of CNNs as the backbone for dense prediction tasks including MDE. Several new techniques are proposed to facilitate dense predictions using self-attention. The code is accessible at: https://github.com/isl-org/DPT.
- SimIPU [34]: A multi-modal contrastive learning framework consists of a simple pertaining strategy that leverages the spatial perception module to learn a spatial-aware representation from images and point clouds. The code is accessible at: https://github.com/zhyever/SimIPU.
- DepthFormer [35]: A monocular model that is tailored to facilitate the long-range correlation for accurate MDE. The backbone is adopted from Vision Transformers, where a hierarchical aggregation and heterogeneous interaction module is proposed to enhance the features via element-wise interaction. The code is accessible at: https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/tree/main/configs/depthformer.