2 citations · 6 across the 7 of their papers we have counts for
8 papers
Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction
Xiangyu Xu, Lichang Chen, Changjiang Cai +6
Direct optimization of interpolated features on multi-resolution voxel grids has emerged as a more efficient alternative to MLP-like modules. However, this approach is constrained…
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation
Zhiqi Zhang, Nitin Bansal, Changjiang Cai +4
Synthetic datasets are often used to pretrain end-to-end optical flow networks, due to the lack of a large amount of labeled, real-scene data. But major drops in accuracy occur whe…
MonoIndoor++:Towards Better Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments
Runze Li, Pan Ji, Yi Xu +1
Self-supervised monocular depth estimation has seen significant progress in recent years, especially in outdoor environments. However, depth prediction results are not satisfying i…
CNN-Augmented Visual-Inertial SLAM with Planar Constraints
Pan Ji, Yuan Tian, Qingan Yan +2
We present a robust visual-inertial SLAM system that combines the benefits of Convolutional Neural Networks (CNNs) and planar constraints. Our system leverages a CNN to predict the…
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras
Qingan Yan, Pan Ji, Nitin Bansal +3
In this paper, we deal with the problem of monocular depth estimation for fisheye cameras in a self-supervised manner. A known issue of self-supervised depth estimation is that it…
GeoRefine: Self-Supervised Online Depth Refinement for Accurate Dense Mapping
Pan Ji, Qingan Yan, Yuxin Ma +1
We present a robust and accurate depth refinement system, named GeoRefine, for geometrically-consistent dense mapping from monocular sequences. GeoRefine consists of three modules:…
RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View Stereo
Changjiang Cai, Pan Ji, Qingan Yan +1
This paper presents a learning-based method for multi-view depth estimation from posed images. Our core idea is a "learning-to-optimize" paradigm that iteratively indexes a plane-s…
PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
Jiachen Liu, Pan Ji, Nitin Bansal +4
We present a novel framework named PlaneMVS for 3D plane reconstruction from multiple input views with known camera poses. Most previous learning-based plane reconstruction methods…