Foundation Models Meet Low-Cost Sensors: Test-Time Adaptation for Rescaling Disparity for Zero-Shot Metric Depth Estimation

The recent development of foundation models for monocular depth estimation such as Depth Anything paved the way to zero-shot monocular depth estimation. Since it returns an affine-invariant disparity map, the favored technique to recover the metric depth consists in fine-tuning the model. However, this stage is costly to perform because of the training but also due to the creation of the dataset. It must contain images captured by the camera that will be used at test time and the corresponding ground truth. Moreover, the fine-tuning may also degrade the generalizing capacity of the original model. Instead, we propose in this paper a new method to rescale Depth Anything predictions using 3D points provided by low-cost sensors or techniques such as low-resolution LiDAR, stereo camera, structure-from-motion where poses are given by an IMU. Thus, this approach avoids fine-tuning and preserves the generalizing power of the original depth estimation model while being robust to the noise of the sensor or of the depth model. Our experiments highlight improvements relative to other metric depth estimation methods and competitive results compared to fine-tuned approaches. Code available at https://gitlab.ensta.fr/ssh/monocular-depth-rescaling.

MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty

Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfections of these approaches, a quantification of the prediction confidence is crucial to guide decision-making systems that rely on depth estimation. In this paper, we propose MonoProb, a new unsupervised monocular depth estimation method that returns an interpretable uncertainty, which means that the uncertainty reflects the expected error of the network in its depth predictions. We rethink the stereo or the structure-from-motion paradigms used to train unsupervised monocular depth models as a probabilistic problem. Within a single forward pass inference, this model provides a depth prediction and a measure of its confidence, without increasing the inference time. We then improve the performance on depth and uncertainty with a novel self-distillation loss for which a student is supervised by a pseudo ground truth that is a probability distribution on depth output by a teacher. To quantify the performance of our models we design new metrics that, unlike traditional ones, measure the absolute performance of uncertainty predictions. Our experiments highlight enhancements achieved by our method on standard depth and uncertainty metrics as well as on our tailored metrics. https://github.com/CEA-LIST/MonoProb