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Found 15 papers, 3 papers with code
CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement
We propose CHOSEN, a simple yet flexible, robust and effective multi-view depth refinement framework.
Light Stage Super-Resolution: Continuous High-Frequency Relighting
The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces.
Neural Light Transport for Relighting and View Synthesis
In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint.
Learning Illumination from Diverse Portraits
We present a learning-based technique for estimating high dynamic range (HDR), omnidirectional illumination from a single low dynamic range (LDR) portrait image captured under arbitrary indoor or outdoor lighting conditions.
Single Image Portrait Relighting
Lighting plays a central role in conveying the essence and depth of the subject in a portrait photograph.
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.
Ranked #2 on
Stereo Depth Estimation
on sceneflow
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems.
Low Compute and Fully Parallel Computer Vision With HashMatch
Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/background segmentation can be formulated as per-pixel image labeling tasks.
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems
Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision.
The Global Patch Collider
In contrast to conventional approaches that rely on pairwise distance computation, our algorithm isolates distinctive pixel pairs that hit the same leaf during traversal through multiple learned tree structures.
HyperDepth: Learning Depth From Structured Light Without Matching
We contribute an algorithm for solving this correspondence problem efficiently, without compromising depth accuracy.
A Light Transport Model for Mitigating Multipath Interference in Time-of-Flight Sensors
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.
A Light Transport Model for Mitigating Multipath Interference in TOF Sensors
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.
Depth Super Resolution by Rigid Body Self-Similarity in 3D
We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map.
