Mar 22, 2024 · We discuss two different deep learning approaches to depth estimation, including an Unsupervised CNN, and Depth Anything. We compare and contrast these approaches, and …

This is built using encoder-decoder with stacked hourglass to estimate depth map. Our proposed model can be divided into two parts. In the first part, encoder-decoder is designed with...

Depth estimation is a crucial task in computer vision, with applications in autonomous driving, robotics, augmented reality, and more. edepth addresses this task by predicting the distance …

In this work, we aim to pro-vide a comprehensive study on the techniques widely used in monocular depth estimation, and examine their individ-ual influence on the performance. More …

AUTODEPTH: SINGLE IMAGE DEPTH MAP ESTIMATION VIA RESIDUAL CNN ENCODER-DECODER AND STACKED HOURGLASS SCEE, Indian Institute of Technology Mandi, India …

May 11, 2023 · In this paper, a new depth estimation method was proposed, which combines a license plate detector with a nonlinear MLP model to estimate the depth of cars within a …

Sep 1, 2019 · Block diagram of our network for depth estimation is consisted of multiple stacked layers with hourglass in encoder-decoder. Visual comparison on Ikea Chair dataset: First left...

Time to train the network is directly proportional to the depth and complexity of the CNN architecture. In further implementations, we plan to combine the architecture of our Patched …

Figure 1 shows the proposed encoder-decoder architecture for depth estimation from a single RGB image. ... ... paper deals with investigating depth estimation of a single RGB image...

Our final model extends an CNN-based encoder-decoder approach to Frame Interpolation. While the perfor-mance of our model is sub-par to the current state-of-the-art techniques, it …