We present a novel differentiable rendering framework for joint geometry...
Generating robust and reliable correspondences across images is a fundam...
Neural implicit functions have recently shown promising results on surfa...
We present a differentiable rendering framework for material and lightin...
Recent works on implicit neural representations have shown promising res...
Matching local features across images is a fundamental problem in comput...
Learning-based stereo matching has recently achieved promising results, ...
In this paper, we introduce a novel network, called discriminative featu...
Current bundle adjustment solvers such as the Levenberg-Marquardt (LM)
a...
Recent learning-based approaches, in which models are trained by single-...
Temporal camera relocalization estimates the pose with respect to each v...
This work focuses on mitigating two limitations in the joint learning of...
A successful point cloud registration often lies on robust establishment...
While deep learning has recently achieved great success on multi-view st...
The self-supervised learning of depth and pose from monocular sequences
...
Establishing correspondences between two images requires both local and
...
Semantic segmentation is pixel-wise classification which retains critica...
Most existing studies on learning local features focus on the patch-base...
Deep learning has recently demonstrated its excellent performance for
mu...
Accurate relative pose is one of the key components in visual odometry (...
Convolutional Neural Networks (CNNs) have achieved superior performance ...
Learned local descriptors based on Convolutional Neural Networks (CNNs) ...
Critical to the registration of point clouds is the establishment of a s...
We present an end-to-end deep learning architecture for depth map infere...
In this paper, we tackle the accurate and consistent Structure from Moti...
Information about the illuminant color is well contained in both achroma...
In this paper, we present a novel affine-invariant feature based on SIFT...