Self-supervised monocular depth estimation is gaining significant attention because it can learn depth from video without needing expensive ground-truth data. However, many self-supervised models remain too heavy for edge devices, and simply shrinking them tends to degrade accuracy. To address this trade-off, we present MonoLENS, an extension of Lite-Mono. MonoLENS follows a design that reduces computation while preserving geometric fidelity (relative depth relations, boundaries, and planar structures). MonoLENS advances Lite-Mono by suppressing computation on paths with low geometric contribution, focusing compute and attention on layers rich in structural cues, and pruning redundant operations in later stages. Our model incorporates two new modules, the DS-Upsampling Block and the MCACoder, along with a simplified encoder. Specifically, the DS-Upsampling Block uses depthwise separable convolutions throughout the decoder, which greatly lowers floating-point operations (FLOPs). Furthermore, the MCACoder applies Multidimensional Collaborative Attention (MCA) to the output of the second encoder stage, helping to make edge details sharper in high-resolution feature maps. Additionally, we simplified the encoder’s architecture by reducing the number of blocks in its fourth stage from 10 to 4, which resulted in a further reduction of model parameters. When tested on both the KITTI and Cityscapes benchmarks, MonoLENS achieved leading performance. On the KITTI benchmark, MonoLENS reduced the number of model parameters by 42% (1.8M) compared with Lite-Mono, while simultaneously improving the squared relative error by approximately 4.5%.

Self-supervised monocular depth estimation has demonstrated high practical utility, as it can be trained using a photometric image reconstruction loss between the original image and a reprojected image generated from the estimated depth and relative pose, thereby alleviating the burden of large-scale label creation. However, this photometric image reconstruction loss relies on the Lambertian reflectance assumption. Under non-Lambertian conditions such as specular reflections or strong illumination gradients, pixel values fluctuate depending on the lighting and viewpoint, which often misguides training and leads to large depth errors. To address this issue, we propose a multitask learning framework that integrates albedo estimation as a supervised auxiliary task. The proposed framework is implemented on top of representative self-supervised monocular depth estimation backbones, including Monodepth2 and Lite-Mono, by adopting a multi-head architecture in which the shared encoder–decoder branches at each upsampling block into a Depth Head and an Albedo Head. Furthermore, we apply Intrinsic Image Decomposition to generate albedo images and design an albedo supervision loss that uses these albedo maps as training targets for the Albedo Head. We then integrate this loss term into the overall training objective, explicitly exploiting illumination-invariant albedo components to suppress erroneous learning in reflective regions and areas with strong illumination gradients. Experiments on the ScanNetV2 dataset demonstrate that, for the lightweight backbone Lite-Mono, our method achieves an average reduction of 18.5% over the four standard depth error metrics and consistently improves accuracy metrics, without increasing the number of parameters and FLOPs at inference time.