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Found 14 papers, 4 papers with code
Neural Texture Puppeteer: A Framework for Neural Geometry and Texture Rendering of Articulated Shapes, Enabling Re-Identification at Interactive Speed
Realistic looking novel view and pose synthesis for different synthetic cow textures further demonstrate the quality of our method.
3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking
To the best of our knowledge we are the first to present a framework for 2D/3D animal posture and trajectory tracking that works in both indoor and outdoor environments for up to 10 individuals.
Towards Monocular Shape from Refraction
In contrast, we claim that a simple energy function based on Snell's law enables the reconstruction of an arbitrary refractive surface geometry using just a single image and known background texture and geometry.
SupeRVol: Super-Resolution Shape and Reflectance Estimation in Inverse Volume Rendering
We propose an end-to-end inverse rendering pipeline called SupeRVol that allows us to recover 3D shape and material parameters from a set of color images in a super-resolution manner.
GenDR: A Generalized Differentiable Renderer
In this work, we present and study a generalized family of differentiable renderers.
Style Agnostic 3D Reconstruction via Adversarial Style Transfer
Recently introduced differentiable renderers can be leveraged to learn the 3D geometry of objects from 2D images, but those approaches require additional supervision to enable the renderer to produce an output that can be compared to the input image.
Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation
To this end, we propose a novel structure-from-motion-aware PatchMatch approach that, in contrast to existing matching techniques, combines two hierarchical feature matching methods: a recent two-frame PatchMatch approach for optical flow estimation (general motion) and a specifically tailored three-frame PatchMatch approach for rigid scene reconstruction (SfM).
Light Field Intrinsics With a Deep Encoder-Decoder Network
We present a fully convolutional autoencoder for light fields, which jointly encodes stacks of horizontal and vertical epipolar plane images through a deep network of residual layers.
Accurate Depth and Normal Maps From Occlusion-Aware Focal Stack Symmetry
We introduce a novel approach to jointly estimate consistent depth and normal maps from 4D light fields, with two main contributions.
What Sparse Light Field Coding Reveals About Scene Structure
In this paper, we propose a novel method for depth estimation in light fields which employs a specifically designed sparse decomposition to leverage the depth-orientation relationship on its epipolar plane images.
On Linear Structure From Motion for Light Field Cameras
We present a novel approach to relative pose estimation which is tailored to 4D light field cameras.
Bayesian View Synthesis and Image-Based Rendering Principles
In this paper, we address the problem of synthesizing novel views from a set of input images.
Globally Consistent Multi-label Assignment on the Ray Space of 4D Light Fields
We present the first variational framework for multi-label segmentation on the ray space of 4D light fields.
The Variational Structure of Disparity and Regularization of 4D Light Fields
Unlike traditional images which do not offer information for different directions of incident light, a light field is defined on ray space, and implicitly encodes scene geometry data in a rich structure which becomes visible on its epipolar plane images.
