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Found 9 papers, 1 papers with code
From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior
Subsequently, in the fine training stage, we refine the DE network to learn the detailed depth of the objects from the reprojection loss, while ensuring accurate DE on the moving object regions by employing our regularization loss with a cost-volume-based weighting factor.
ProNeRF: Learning Efficient Projection-Aware Ray Sampling for Fine-Grained Implicit Neural Radiance Fields
Recent advances in neural rendering have shown that, albeit slow, implicit compact models can learn a scene's geometries and view-dependent appearances from multiple views.
Novel View Synthesis with View-Dependent Effects from a Single Image
In this paper, we firstly consider view-dependent effects into single image-based novel view synthesis (NVS) problems.
Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videos
Recently, much attention has been drawn to learning the underlying 3D structures of a scene from monocular videos in a fully self-supervised fashion.
PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss
Our PLADE-Net is based on a new network architecture with neural positional encoding and a novel loss function that borrows from the closed-form solution of the matting Laplacian to learn pixel-level accurate depth estimation from stereo images.
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations
Our proposed network architecture, the monster-net, is devised with a novel t-shaped adaptive kernel with globally and locally adaptive dilation, which can efficiently incorporate global camera shift into and handle local 3D geometries of the target image's pixels for the synthesis of naturally looking 3D panned views when a 2-D input image is given.
Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations
Our proposed network architecture, the monster-net, is devised with a novel "t-shaped" adaptive kernel with globally and locally adaptive dilation, which can efficiently incorporate global camera shift into and handle local 3D geometries of the target image's pixels for the synthesis of naturally looking 3D panned views when a 2-D input image is given.
Deep 3D-Zoom Net: Unsupervised Learning of Photo-Realistic 3D-Zoom
The 3D-zoom operation is the positive translation of the camera in the Z-axis, perpendicular to the image plane.
A Novel Monocular Disparity Estimation Network with Domain Transformation and Ambiguity Learning
Convolutional neural networks (CNN) have shown state-of-the-art results for low-level computer vision problems such as stereo and monocular disparity estimations, but still, have much room to further improve their performance in terms of accuracy, numbers of parameters, etc.
