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Found 10 papers, 1 papers with code
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions
Depth estimation under adverse conditions remains a significant challenge.
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
However, given that most of the data in the autonomous driving dataset is collected in daytime scenarios, this leads to poor depth model performance in the face of out-of-distribution(OoD) data.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios
Monocular depth estimation from RGB images plays a pivotal role in 3D vision.
SDGE: Stereo Guided Depth Estimation for 360$^\circ$ Camera Sets
We suggest building virtual pinhole cameras to resolve the distortion problem of fisheye cameras and unify the processing for the two types of 360$^\circ$ cameras.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
During the iterative update, the results of depth estimation are compared across cameras and the information of overlapping areas is propagated to the whole depth maps with the help of basis formulation.
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices
Notably, our solution named LiteDepth ranks 2nd in the MAI&AIM2022 Monocular Depth Estimation Challenge}, with a si-RMSE of 0. 311, an RMSE of 3. 79, and the inference time is 37$ms$ tested on the Raspberry Pi 4.
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data
In this paper, we propose a novel weakly-supervised framework to train a monocular depth estimation network to generate HR depth maps with resolution-mismatched supervision, i. e., the inputs are HR color images and the ground-truth are low-resolution (LR) depth maps.
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
