3D Depth Estimation
12 papers with code • 1 benchmarks • 8 datasets
Image: monodepth2
Datasets
Latest papers
Towards a Robust Framework for NeRF Evaluation
Additionally, we propose a novel metric to measure task complexity of the framework which accounts for the visual parameters and the distribution of the spatial data.
Putting People in their Place: Monocular Regression of 3D People in Depth
To do so, we exploit a 3D body model space that lets BEV infer shapes from infants to adults.
Monocular, One-stage, Regression of Multiple 3D People
Through a body-center-guided sampling process, the body mesh parameters of all people in the image are easily extracted from the Mesh Parameter map.
SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view.
Coherent Reconstruction of Multiple Humans from a Single Image
Our goal is to train a single network that learns to avoid these problems and generate a coherent 3D reconstruction of all the humans in the scene.
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Reliable and accurate 3D object detection is a necessity for safe autonomous driving.
Spherical View Synthesis for Self-Supervised 360 Depth Estimation
This has led to the utilization of view synthesis as an indirect objective for learning depth estimation using efficient data acquisition procedures.
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case.
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images.
A Deep Generative Model for Graph Layout
To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models.