11 citations · 15 across the 9 of their papers we have counts for
11 papers
ActiveGAMER: Active GAussian Mapping through Efficient Rendering
Liyan Chen, Huangying Zhan, Kevin Chen +4
We introduce ActiveGAMER, an active mapping system that utilizes 3D Gaussian Splatting (3DGS) to achieve high-quality, real-time scene mapping and exploration. Unlike traditional N…
NARUTO: Neural Active Reconstruction from Uncertain Target Observations
Ziyue Feng, Huangying Zhan, Zheng Chen +6
We present NARUTO, a neural active reconstruction system that combines a hybrid neural representation with uncertainty learning, enabling high-fidelity surface reconstruction. Our…
PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields
Zheng Chen, Qingan Yan, Huangying Zhan +7
Identifying spatially complete planar primitives from visual data is a crucial task in computer vision. Prior methods are largely restricted to either 2D segment recovery or simpli…
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…
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…
Do End-to-end Stereo Algorithms Under-utilize Information?
Changjiang Cai, Philippos Mordohai
Deep networks for stereo matching typically leverage 2D or 3D convolutional encoder-decoder architectures to aggregate cost and regularize the cost volume for accurate disparity es…
Matching-space Stereo Networks for Cross-domain Generalization
Changjiang Cai, Matteo Poggi, Stefano Mattoccia +1
End-to-end deep networks represent the state of the art for stereo matching. While excelling on images framing environments similar to the training set, major drops in accuracy occ…
Towards Robust RGB-D Human Mesh Recovery
Ren Li, Changjiang Cai, Georgios Georgakis +3
We consider the problem of human pose estimation. While much recent work has focused on the RGB domain, these techniques are inherently under-constrained since there can be many 3D…
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai
Recently, there has been a paradigm shift in stereo matching with learning-based methods achieving the best results on all popular benchmarks. The success of these methods is due t…