Learning to fuse monocular and multi-view cues for multi-frame depth estimation in dynamic scenes
Multi-frame depth estimation generally achieves high accuracy relying on the multi-view
geometric consistency. When applied in dynamic scenes, eg, autonomous driving, this …
geometric consistency. When applied in dynamic scenes, eg, autonomous driving, this …
Digging into depth priors for outdoor neural radiance fields
Neural Radiance Fields (NeRFs) have demonstrated impressive performance in vision and
graphics tasks, such as novel view synthesis and immersive reality. However, the shape …
graphics tasks, such as novel view synthesis and immersive reality. However, the shape …
Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning
Recovering the 3D scene geometry from a single view is a fundamental yet ill-posed
problem in computer vision. While classical depth estimation methods infer only a 2.5 D …
problem in computer vision. While classical depth estimation methods infer only a 2.5 D …
SENSE: Self-Evolving Learning for Self-Supervised Monocular Depth Estimation
Self-supervised depth estimation methods can achieve competitive performance using only
unlabeled monocular videos, but they suffer from the uncertainty of jointly learning depth …
unlabeled monocular videos, but they suffer from the uncertainty of jointly learning depth …
Self-supervised monocular depth estimation with frequency-based recurrent refinement
Self-supervised monocular depth estimation has succeeded in learning scene geometry
from only image pairs or sequences. However, it is still highly ill-posed for self-supervised …
from only image pairs or sequences. However, it is still highly ill-posed for self-supervised …
Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance
Self-supervised monocular depth estimation has shown great success in learning depth
using only images for supervision. In this paper, we propose to enhance self-supervised …
using only images for supervision. In this paper, we propose to enhance self-supervised …
Frequency-aware self-supervised monocular depth estimation
X Chen, TH Li, R Zhang, G Li - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
We present two versatile methods to generally enhance self-supervised monocular depth
estimation (MDE) models. The high generalizability of our methods is achieved by solving …
estimation (MDE) models. The high generalizability of our methods is achieved by solving …
Non-uniform motion deblurring with blurry component divided guidance
Blind image deblurring is a fundamental and challenging computer vision problem, which
aims to recover both the blur kernel and the latent sharp image from only a blurry …
aims to recover both the blur kernel and the latent sharp image from only a blurry …
GRAN: ghost residual attention network for single image super resolution
Recently, many works have designed wider and deeper networks to achieve higher image
super-resolution performance. Despite their outstanding performance, they still suffer from …
super-resolution performance. Despite their outstanding performance, they still suffer from …
SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Model
In the last year, universal monocular metric depth estimation (universal MMDE) has gained
considerable attention, serving as the foundation model for various multimedia tasks, such …
considerable attention, serving as the foundation model for various multimedia tasks, such …