Search Results for author:
Found 18 papers, 11 papers with code
PointNeRF++: A multi-scale, point-based Neural Radiance Field
We overcome these problems with a simple representation that aggregates point clouds at multiple scale levels with sparse voxel grids at different resolutions.
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding
Semantic 2D maps are commonly used by humans and machines for navigation purposes, whether it's walking or driving.
GECCO: Geometrically-Conditioned Point Diffusion Models
Diffusion models generating images conditionally on text, such as Dall-E 2 and Stable Diffusion, have recently made a splash far beyond the computer vision community.
TUSK: Task-Agnostic Unsupervised Keypoints
Existing unsupervised methods for keypoint learning rely heavily on the assumption that a specific keypoint type (e. g. elbow, digit, abstract geometric shape) appears only once in an image.
COTR: Correspondence Transformer for Matching Across Images
We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other.
Ranked #1 on
Dense Pixel Correspondence Estimation
on KITTI 2012
Dense Pixel Correspondence Estimation
Optical Flow Estimation
DISK: Learning local features with policy gradient
Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints.
Ranked #2 on
Image Matching
on IMC PhotoTourism
(using extra training data)
Image Matching across Wide Baselines: From Paper to Practice
We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task -- the accuracy of the reconstructed camera pose -- as our primary metric.
Beyond Cartesian Representations for Local Descriptors
We demonstrate that this representation is particularly amenable to learning descriptors with deep networks.
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds.
Linearized Multi-Sampling for Differentiable Image Transformation
We propose a novel image sampling method for differentiable image transformation in deep neural networks.
LF-Net: Learning Local Features from Images
We present a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision.
Learning to Find Good Correspondences
We develop a deep architecture to learn to find good correspondences for wide-baseline stereo.
Learning to Match Aerial Images With Deep Attentive Architectures
We demonstrate that our models outperform the state-of-the-art on ultra-wide baseline matching and approach human accuracy.
LIFT: Learned Invariant Feature Transform
We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description.
Discriminative Learning of Deep Convolutional Feature Point Descriptors
Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e. g. SIFT.
Ranked #2 on
Satellite Image Classification
on SAT-4
Fracking Deep Convolutional Image Descriptors
In this paper we propose a novel framework for learning local image descriptors in a discriminative manner.
Segmentation-aware Deformable Part Models
In this work we propose a technique to combine bottom-up segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs).
Dense Segmentation-Aware Descriptors
In this work we exploit segmentation to construct appearance descriptors that can robustly deal with occlusion and background changes.
