120 citations · 147 across the 7 of their papers we have counts for
19 papers · 1 filter
Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data
Nilesh Kulkarni, Linyi Jin, Justin Johnson +1
We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data. At test time, our system maps a previously unseen RGB ima…
Self-Supervised Correspondence Estimation via Multiview Registration
Mohamed El Banani, Ignacio Rocco, David Novotny +4
Video provides us with the spatio-temporal consistency needed for visual learning. Recent approaches have utilized this signal to learn correspondence estimation from close-by fram…
PixelSynth: Generating a 3D-Consistent Experience from a Single Image
Chris Rockwell, David F. Fouhey, Justin Johnson
Recent advancements in differentiable rendering and 3D reasoning have driven exciting results in novel view synthesis from a single image. Despite realistic results, methods are li…
Inverting and Understanding Object Detectors
Ang Cao, Justin Johnson
As a core problem in computer vision, the performance of object detection has improved drastically in the past few years. Despite their impressive performance, object detectors suf…
Bootstrap Your Own Correspondences
Mohamed El Banani, Justin Johnson
Geometric feature extraction is a crucial component of point cloud registration pipelines. Recent work has demonstrated how supervised learning can be leveraged to learn better and…
Rethinking "Batch" in BatchNorm
Yuxin Wu, Justin Johnson
BatchNorm is a critical building block in modern convolutional neural networks. Its unique property of operating on "batches" instead of individual samples introduces significantly…