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Found 14 papers, 9 papers with code
MonoPatchNeRF: Improving Neural Radiance Fields with Patch-based Monocular Guidance
Our experiments show 4x the performance of RegNeRF and 8x that of FreeNeRF on average F1@2cm for ETH3D MVS benchmark, suggesting a fruitful research direction to improve the geometric accuracy of NeRF-based models, and sheds light on a potential future approach to enable NeRF-based optimization to eventually outperform traditional MVS.
QFF: Quantized Fourier Features for Neural Field Representations
Instead, we propose to encode features in bins of Fourier features that are commonly used for positional encoding.
Deep PatchMatch MVS with Learned Patch Coplanarity, Geometric Consistency and Adaptive Pixel Sampling
Recent work in multi-view stereo (MVS) combines learnable photometric scores and regularization with PatchMatch-based optimization to achieve robust pixelwise estimates of depth, normals, and visibility.
Recurrent Transformer Variational Autoencoders for Multi-Action Motion Synthesis
We consider the problem of synthesizing multi-action human motion sequences of arbitrary lengths.
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility
To overcome the challenge of the non-differentiable PatchMatch optimization that involves iterative sampling and hard decisions, we use reinforcement learning to minimize expected photometric cost and maximize likelihood of ground truth depth and normals.
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention
We embed the attention module in a ``slow-fast'' architecture, where the slower network runs on sparsely sampled keyframes and the light-weight shallow network runs on non-keyframes at a high frame rate.
Improving Style Transfer with Calibrated Metrics
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image.
Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods
Recent approaches for predicting layouts from 360 panoramas produce excellent results.
Counterfactual Depth from a Single RGB Image
We describe a method that predicts, from a single RGB image, a depth map that describes the scene when a masked object is removed - we call this "counterfactual depth" that models hidden scene geometry together with the observations.
Silhouette Guided Point Cloud Reconstruction beyond Occlusion
One major challenge in 3D reconstruction is to infer the complete shape geometry from partial foreground occlusions.
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e. g. L-shape room).
Complete 3D Scene Parsing from an RGBD Image
In this paper, we aim to interpret indoor scenes from one RGBD image.
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data.
Predicting Complete 3D Models of Indoor Scenes
One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors.
