activity
20152024
most citedDeep Closest Point: Learning Representations for Point Cloud Registration

120 citations · 433 across the 25 of their papers we have counts for

collaborators
Showing 2020Show all

11 papers · 1 filter

math.ST2020

-Variance: A Clustered Notion of Variance

Justin Solomon, Kristjan Greenewald, Haikady N. Nagaraja

We introduce -variance, a generalization of variance built on the machinery of random bipartite matchings. -variance measures the expected cost of matching two sets of sa…

stat.ME2020

Outlier-Robust Optimal Transport

Debarghya Mukherjee, Aritra Guha, Justin Solomon +2

Optimal transport (OT) measures distances between distributions in a way that depends on the geometry of the sample space. In light of recent advances in computational OT, OT dista…

cs.DS2020

Redistricting Algorithms

Amariah Becker, Justin Solomon

Why not have a computer just draw a map? This is something you hear a lot when people talk about gerrymandering, and it's easy to think at first that this could solve redistricting…

cs.CV20209 cited

Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection

Yue Wang, Alireza Fathi, Jiajun Wu +2

A common dilemma in 3D object detection for autonomous driving is that high-quality, dense point clouds are only available during training, but not testing. We use knowledge distil…

cs.LG2020

Continuous Regularized Wasserstein Barycenters

Lingxiao Li, Aude Genevay, Mikhail Yurochkin +1

Wasserstein barycenters provide a geometrically meaningful way to aggregate probability distributions, built on the theory of optimal transport. They are difficult to compute in pr…

cs.CV202029 cited

Pillar-based Object Detection for Autonomous Driving

Yue Wang, Alireza Fathi, Abhijit Kundu +4

We present a simple and flexible object detection framework optimized for autonomous driving. Building on the observation that point clouds in this application are extremely sparse…