most citedHi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping

6 citations · 13 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI20241 cited

SEAL: SEmantic-Augmented Imitation Learning via Language Model

Chengyang Gu, Yuxin Pan, Haotian Bai +2

Hierarchical Imitation Learning (HIL) is a promising approach for tackling long-horizon decision-making tasks. While it is a challenging task due to the lack of detailed supervisor…

cs.CV2024

High-Fidelity Mask-free Neural Surface Reconstruction for Virtual Reality

Haotian Bai, Yize Chen, Lin Wang

Object-centric surface reconstruction from multi-view images is crucial in creating editable digital assets for AR/VR. Due to the lack of geometric constraints, existing methods, e…

cs.CV20246 cited

Hi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping

Tongyan Hua, Haotian Bai, Zidong Cao +3

In this paper, we introduce Hi-Map, a novel monocular dense mapping approach based on Neural Radiance Field (NeRF). Hi-Map is exceptional in its capacity to achieve efficient and h…

cs.CV2023

Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF

Haotian Bai, Yiqi Lin, Yize Chen +1

The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtu…

cs.CV20234 cited

FMapping: Factorized Efficient Neural Field Mapping for Real-Time Dense RGB SLAM

Tongyan Hua, Haotian Bai, Zidong Cao +1

In this paper, we introduce FMapping, an efficient neural field mapping framework that facilitates the continuous estimation of a colorized point cloud map in real-time dense RGB S…

cs.CV20232 cited

Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective

Jinjing Zhu, Haotian Bai, Lin Wang

Endeavors have been recently made to leverage the vision transformer (ViT) for the challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross-attention i…