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Found 23 papers, 12 papers with code
DPSNet: End-to-end Deep Plane Sweep Stereo
The cost volume is constructed using a differentiable warping process that allows for end-to-end training of the network.
High-fidelity 3D Human Digitization from Single 2K Resolution Images
The low-resolution depth network predicts the global structure from a low-resolution image, and the part-wise image-to-normal network predicts the details of the 3D human body structure.
High-Quality Depth From Uncalibrated Small Motion Clip
We propose a novel approach that generates a high-quality depth map from a set of images captured with a small viewpoint variation, namely small motion clip.
EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images
Light field cameras capture both the spatial and the angular properties of light rays in space.
Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction
A key idea of GP-Graph is to model both individual-wise and group-wise relations as graph representations.
EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting
In this paper, we present EigenTrajectory ($\mathbb{ET}$), a trajectory prediction approach that uses a novel trajectory descriptor to form a compact space, known here as $\mathbb{ET}$ space, in place of Euclidean space, for representing pedestrian movements.
Non-Probability Sampling Network for Stochastic Human Trajectory Prediction
Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to infer a finite set of future trajectories.
Learning Depth from Focus in the Wild
In addition, for the generalization of the proposed network, we develop a simulator to realistically reproduce the features of commercial cameras, such as changes in field of view, focal length and principal points.
Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction
Next, to guide the language model in understanding and reasoning high-level knowledge, such as scene context and social relationships between pedestrians, we introduce an auxiliary multi-task question and answering.
SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model
In this paper, we propose SingularTrajectory, a diffusion-based universal trajectory prediction framework to reduce the performance gap across the five tasks.
Task-specific Scene Structure Representations
Understanding the informative structures of scenes is essential for low-level vision tasks.
Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation
Compared to conventional DA methods, our ECB achieves superior performance, which verifies its effectiveness in this hybrid model.
Ranked #3 on
Unsupervised Domain Adaptation
on Office-Home
Robust Depth Estimation from Auto Bracketed Images
As demand for advanced photographic applications on hand-held devices grows, these electronics require the capture of high quality depth.
Accurate Depth Map Estimation From a Lenslet Light Field Camera
This paper introduces an algorithm that accurately estimates depth maps using a lenslet light field camera.
Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions
Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability.
Noise Robust Depth From Focus Using a Ring Difference Filter
With the result from the FM, the role of a DfF pipeline is to determine and recalculate unreliable measurements while enhancing those that are reliable.
High Quality Structure From Small Motion for Rolling Shutter Cameras
To address these problems, we introduce a novel 3D reconstruction method from narrow-baseline image sequences that effectively handles the effects of a rolling shutter that occur from most of commercial digital cameras.
Complementary Sets of Shutter Sequences for Motion Deblurring
In this paper, we present a novel multi-image motion deblurring method utilizing the coded exposure technique.
Explainable Semantic Mapping for First Responders
One of the key challenges in the semantic mapping problem in postdisaster environments is how to analyze a large amount of data efficiently with minimal supervision.
Facial Depth and Normal Estimation using Single Dual-Pixel Camera
Many mobile manufacturers recently have adopted Dual-Pixel (DP) sensors in their flagship models for faster auto-focus and aesthetic image captures.
AI-based automated Meibomian gland segmentation, classification and reflection correction in infrared Meibography
Purpose: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.
Self-Supervised 3D Traversability Estimation with Proxy Bank Guidance
To mitigate the uncertainty, we introduce a deep metric learning-based method to incorporate unlabeled data with a few positive and negative prototypes in order to leverage the uncertainty, which jointly learns using semantic segmentation and traversability regression.
Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data
Our model provides predictions at 6-hour intervals for up to 72 hours in advance and outperforms both state-of-the-art data-driven methods and numerical weather prediction models.
