92 citations · 153 across the 5 of their papers we have counts for
9 papers
What Matters in Range View 3D Object Detection
Benjamin Wilson, Nicholas Autio Mitchell, Jhony Kaesemodel Pontes +1
Lidar-based perception pipelines rely on 3D object detection models to interpret complex scenes. While multiple representations for lidar exist, the range-view is enticing since it…
Fast Neural Scene Flow
Xueqian Li, Jianqiao Zheng, Francesco Ferroni +2
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with d…
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
Benjamin Wilson, William Qi, Tanmay Agarwal +10
We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequ…
Neural Scene Flow Prior
Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
Before the deep learning revolution, many perception algorithms were based on runtime optimization in conjunction with a strong prior/regularization penalty. A prime example of thi…
Scene Flow from Point Clouds with or without Learning
Jhony Kaesemodel Pontes, James Hays, Simon Lucey
Scene flow is the three-dimensional (3D) motion field of a scene. It provides information about the spatial arrangement and rate of change of objects in dynamic environments. Curre…
PointNetLK Revisited
Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied…
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference
Mateusz Michalkiewicz, Jhony K. Pontes, Dominic Jack +2
Existing 3D surface representation approaches are unable to accurately classify pixels and their orientation lying on the boundary of an object. Thus resulting in coarse representa…
Learning Free-Form Deformations for 3D Object Reconstruction
Dominic Jack, Jhony K. Pontes, Sridha Sridharan +4
Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge. Most existing work addresses this issue by employing…
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
Jhony K. Pontes, Chen Kong, Sridha Sridharan +3
One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks. Recent works have relied on volumetric or point cloud representat…