In this paper, we propose a fully differentiable, interpretable, and lightweight monocular VIO model that contains only 4 trainable parameters.
However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization.
Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging.
NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural radiance field to RGB images.
In contrast to voxel-based representations, radiance fields are not confined to a coarse discretization of the 3D space, yet allow for disentangling camera and scene properties while degrading gracefully in the presence of reconstruction ambiguity.
Ranked #2 on Scene Generation on VizDoom
We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain.
Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (e. g., TSDF) from which 3D surface meshes must be extracted in a post-processing step (e. g., via the marching cubes algorithm).
Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better.
Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera.
As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples.
Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.
We consider the problem of image representation for the tasks of unsupervised learning and semi-supervised learning.