Search Results for author:
Found 10 papers, 3 papers with code
SeaThru-NeRF: Neural Radiance Fields in Scattering Media
Even more excitingly, we can render clear views of these scenes, removing the medium between the camera and the scene and reconstructing the appearance and depth of far objects, which are severely occluded by the medium.
NAN: Noise-Aware NeRFs for Burst-Denoising
Such assumptions are not realistic in the presence of large motion and high levels of noise.
The Self-Optimal-Transport Feature Transform
The Self-Optimal-Transport (SOT) feature transform is designed to upgrade the set of features of a data instance to facilitate downstream matching or grouping related tasks.
Few-Shot Image Classification
Large-Scale Person Re-Identification
Generative Adversarial Networks via a Composite Annealing of Noise and Diffusion
Based on these observations, we propose a data representation for the GAN training, called noisy scale-space (NSS), that recursively applies the smoothing with a balanced noise to data in order to replace the high-frequency information by random data, leading to a coarse-to-fine training of GANs.
OATM: Occlusion Aware Template Matching by Consensus Set Maximization
We present a novel approach to template matching that is efficient, can handle partial occlusions, and comes with provable performance guarantees.
Latent RANSAC
We present a method that can evaluate a RANSAC hypothesis in constant time, i. e. independent of the size of the data.
Deleting and Testing Forbidden Patterns in Multi-Dimensional Arrays
For any $\epsilon \in [0, 1]$ and any large enough pattern $P$ over any alphabet, other than a very small set of exceptional patterns, we design a tolerant tester that distinguishes between the case that the distance is at least $\epsilon$ and the case that it is at most $a_d \epsilon$, with query complexity and running time $c_d \epsilon^{-1}$, where $a_d < 1$ and $c_d$ depend only on $d$.
Peeking Template Matching for Depth Extension
We demonstrate on real-world data that our algorithm is capable of completing a full 3D scene from a single depth image and can synthesize a full depth map from a novel viewpoint of the scene.
Inverting RANSAC: Global Model Detection via Inlier Rate Estimation
This work presents a novel approach for detecting inliers in a given set of correspondences (matches).
FasT-Match: Fast Affine Template Matching
Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure.
