Complete point clouds with very few points using Wasserstein GAN and Transformers with Wu

Get 3D models of objects in scene from multiple images without labeling by using NeRF with ONeRF

Get shape, pose, and appearance from a single image using signed distance function with Pavllo

Segment scene in new domain by combining probabilities from old domains with MDA

Improve 3D object detection by combine image and point data at many scales with ImLiDAR

Segment long videos with transformers using tracking information with ROVIS

Counting objects in image with few or zero examples using shape and image features with LOCA

3D object detection in point clouds using only 2D image labels for training with RCV

Survey of computer vision using transformers by Jamil

Better image segmentation of details by using multiple image crops with CropFormer

Improve match 3D point clouds using graph matching with RGM

Re-identify people in new domains with unsupervised learning by rewinding video with CycAs

Real-time multi-person pose estimation and tracking with AlphaPose

Better map 2D image points of people to 3D surfaces using novel loss function with UV R-CNN

Improve 3D object segmentation with scene labels by using superpoints with WHCN

Better object detection with few examples by spatial reasoning on known objects with FSOD-SR

Improve 3D object segmentation using transformers directly on point clouds with Mask3D

Survey of 3D reconstruction of non-rigid objects using monocular images

Object segmentation by label only 1 point per target when train with PSPS

Real-time 3D scene reconstruction from monocular images using SLAM for NeRF with NeRF-SLAM