Monocular depth estimation is a computer vision task that involves predicting the depth information of a scene from a single image.
This is the reference PyTorch implementation for training and testing depth estimation models using the method described in MonoViT.
Mar 13, 2024 · We propose METER, a novel lightweight vision transformer architecture capable of achieving state of the art estimations and low latency inference performances.
Use Cases. Depth estimation models can be used to estimate the depth of different objects present in an image. Estimation of Volumetric Information.
STereo TRansformer (STTR) revisits stereo depth estimation from a sequence-to-sequence perspective. The network combines conventional CNN feature extractor and ...
Jun 9, 2023 · In this paper, we present a Token-Sharing Transformer (TST), an architecture using the Transformer for monocular depth estimation, optimized especially in ...
Multi-frame depth estimation improves over single- frame approaches by also leveraging geometric relation- ships between images via feature matching, ...
Aug 7, 2024 · The Dense Prediction Transformer (DPT) harnesses these advantages to deliver high-resolution depth maps with fine-grained details. By combining ...
In this paper, we propose a hybrid network with a Transformer-based encoder and a CNN-based decoder for monocular depth estimation. The encoder follows the ...
Jul 25, 2023 · In this article, you will learn the concept of Dense Prediction Transformers (DPTs) and their role in image depth estimation.