Search Results for author:
Found 13 papers, 6 papers with code
NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation
3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints.
Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane
We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass.
BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation
A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed.
RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery
While showing promising results, recent RGB-D camera-based category-level object pose estimation methods have restricted applications due to the heavy reliance on depth sensors.
Multi-view Tracking Using Weakly Supervised Human Motion Prediction
Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes.
Ranked #2 on
Multi-Object Tracking
on Wildtrack
Learning to Align Sequential Actions in the Wild
To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions.
Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning
We introduce a novel approach to unsupervised and semi-supervised domain adaptation for semantic segmentation.
Leveraging Self-Supervision for Cross-Domain Crowd Counting
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
Counting People by Estimating People Flows
Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images.
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
Estimating People Flows to Better Count Them in Crowded Scenes
In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing.
Context-Aware Crowd Counting
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density.
Ranked #1 on
Crowd Counting
on Venice
Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation
In this paper, we explicitly model the scale changes and reason in terms of people per square-meter.
