activity
20162023
most citedScene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning

4 citations · 5 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2024

InSpaceType: Dataset and Benchmark for Reconsidering Cross-Space Type Performance in Indoor Monocular Depth

Cho-Ying Wu, Quankai Gao, Chin-Cheng Hsu +3

Indoor monocular depth estimation helps home automation, including robot navigation or AR/VR for surrounding perception. Most previous methods primarily experiment with the NYUv2 D…

cs.CV20231 cited

MMVP: Motion-Matrix-based Video Prediction

Yiqi Zhong, Luming Liang, Ilya Zharkov +1

A central challenge of video prediction lies where the system has to reason the objects' future motions from image frames while simultaneously maintaining the consistency of their…

cs.CV2023

Strivec: Sparse Tri-Vector Radiance Fields

Quankai Gao, Qiangeng Xu, Hao Su +2

We propose Strivec, a novel neural representation that models a 3D scene as a radiance field with sparsely distributed and compactly factorized local tensor feature grids. Our appr…

cs.CV2022

Aware of the History: Trajectory Forecasting with the Local Behavior Data

Yiqi Zhong, Zhenyang Ni, Siheng Chen +1

The historical trajectories previously passing through a location may help infer the future trajectory of an agent currently at this location. Despite great improvements in traject…

cs.CV2021

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

Qiangeng Xu, Yiqi Zhong, Ulrich Neumann

Advances in LiDAR sensors provide rich 3D data that supports 3D scene understanding. However, due to occlusion and signal miss, LiDAR point clouds are in practice 2.5D as they cove…

cs.CV20164 cited

Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning

Qiangui Huang, Weiyue Wang, Kevin Zhou +2

Recurrent neural network (RNN), as a powerful contextual dependency modeling framework, has been widely applied to scene labeling problems. However, this work shows that directly a…