Stereo Depth Estimation
46 papers with code • 5 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Stereo Depth Estimation
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Libraries
Use these libraries to find Stereo Depth Estimation models and implementationsDatasets
KITTI
Spring
GUISS dataset
VBR
Latest papers with no code
Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder
Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability.
Temporally Consistent Online Depth Estimation in Dynamic Scenes
We present a framework named Consistent Online Dynamic Depth (CODD) to produce temporally consistent depth estimates in dynamic scenes in an online setting.
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion
In this paper, we show that effective feature-level collaboration of the networks for the three respective tasks could achieve much greater performance improvement for all three tasks than only loss-level joint optimization.
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry
To enforce the epipolar constraint, the mutual epipolar attention mechanism has been designed which gives more emphasis to correspondences of features which lie on the same epipolar line while learning mutual information between the input stereo pair.
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup
Our experiments show that this SNN architecture, composed of coincidence detectors and disparity sensitive neurons, is able to provide a coarse estimate of the input disparity instantaneously, thereby detecting the presence of a stimulus moving in depth in real-time.
Event-Intensity Stereo: Estimating Depth by the Best of Both Worlds
Event cameras can report scene movements as an asynchronous stream of data called the events.
Joint Pruning & Quantization for Extremely Sparse Neural Networks
We investigate pruning and quantization for deep neural networks.
Robust Vision Using Retro Reflective Markers for Remote Handling in ITER
The International Thermonuclear Experimental Reactor (ITER)'s working environment is characterized by extreme conditions, that deem maintenance and inspection tasks to be carried out through remote handling.
StereoDRNet: Dilated Residual StereoNet
We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reconstruction of a scene.
Online Adaptation through Meta-Learning for Stereo Depth Estimation
Our proposal is evaluated on the wellestablished KITTI dataset, where we show that our online method is competitive withstate of the art algorithms trained in a batch setting.
