Disparity Estimation
51 papers with code • 4 benchmarks • 4 datasets
The Disparity Estimation is the task of finding the pixels in the multiscopic views that correspond to the same 3D point in the scene.
Latest papers with no code
Iterative Occlusion-Aware Light Field Depth Estimation using 4D Geometrical Cues
This is possible because the 3D information of a scene is embedded in the 4D light field geometry.
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging
Our resulting CADS imaging system demonstrates improvement of $>$1. 5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings.
Left-right Discrepancy for Adversarial Attack on Stereo Networks
Stereo matching neural networks often involve a Siamese structure to extract intermediate features from left and right images.
Color Agnostic Cross-Spectral Disparity Estimation
The theoretical examination of the novel color agnostic method is completed by an extensive evaluation compared to state of the art including self-recorded multispectral data and a reference implementation.
Improving Stability during Upsampling -- on the Importance of Spatial Context
While during downsampling, aliases and artifacts can be reduced by blurring feature maps, the emergence of fine details is crucial during upsampling.
SOccDPT: Semi-Supervised 3D Semantic Occupancy from Dense Prediction Transformers trained under memory constraints
To address the limitations of existing methods trained on structured traffic datasets, we train our model on unstructured datasets including the Indian Driving Dataset and Bengaluru Driving Dataset.
Learning based Deep Disentangling Light Field Reconstruction and Disparity Estimation Application
In this paper, we propose a Deep Disentangling Mechanism, which inherits the principle of the light field disentangling mechanism and further develops the design of the feature extractor and adds advanced network structure.
Online Adaptive Disparity Estimation for Dynamic Scenes in Structured Light Systems
In recent years, deep neural networks have shown remarkable progress in dense disparity estimation from dynamic scenes in monocular structured light systems.
IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images
Single-shot face anti-spoofing (FAS) is a key technique for securing face recognition systems, and it requires only static images as input.
Video Frame Interpolation with Stereo Event and Intensity Camera
The stereo event-intensity camera setup is widely applied to leverage the advantages of both event cameras with low latency and intensity cameras that capture accurate brightness and texture information.