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Found 13 papers, 8 papers with code
AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields
However, rendering images with this new paradigm is slow due to the fact that an accurate quadrature of the volume rendering equation requires a large number of samples for each ray.
LiveView: Dynamic Target-Centered MPI for View Synthesis
Existing Multi-Plane Image (MPI) based view-synthesis methods generate an MPI aligned with the input view using a fixed number of planes in one forward pass.
Neural 3D Video Synthesis from Multi-view Video
We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation.
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame.
SENSE: a Shared Encoder Network for Scene-flow Estimation
We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion estimation, and semantic segmentation.
miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework
Many problems in computer vision and robotics can be phrased as non-linear least squares optimization problems represented by factor graphs, for example, simultaneous localization and mapping (SLAM), structure from motion (SfM), motion planning, and control.
Multi-class Classification without Multi-class Labels
This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation.
Taking a Deeper Look at the Inverse Compositional Algorithm
In this paper, we provide a modern synthesis of the classic inverse compositional algorithm for dense image alignment.
A probabilistic constrained clustering for transfer learning and image category discovery
The proposed objective directly minimizes the negative log-likelihood of cluster assignment with respect to the pairwise constraints, has no hyper-parameters, and demonstrates improved scalability and performance on both supervised learning and unsupervised transfer learning.
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Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems.
Learning to cluster in order to transfer across domains and tasks
The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning.
Deep Image Category Discovery using a Transferred Similarity Function
We propose that this network can be learned with contrastive loss which is only based on weak binary pair-wise constraints.
A Continuous Optimization Approach for Efficient and Accurate Scene Flow
We propose a continuous optimization method for solving dense 3D scene flow problems from stereo imagery.
