MSI-NeRF: Linking Omni-Depth with View Synthesis through Multi-Sphere Image aided Generalizable Neural Radiance Field
Dongyu Yan, Guanyu Huang, Fengyu Quan, Haoyao Chen.
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025)
Arxiv / Paper / Video / Github

We create a generalizable NeRF that takes four fisheye images as input and outputs a 3D MSI representation for novel view synthesis and depth estimation. It can be trained with synthetic depth data only and can generalize to a wide range of scenarios. We also released a fisheye multi-view dataset for training and evaluation.

Active Implicit Object Reconstruction using Uncertainty-guided Next-Best-View Optimization
Dongyu Yan*, Jianheng Liu*, Fengyu Quan, Haoyao Chen.
IEEE Robotics and Automation Letters (RA-L 2023)
Arxiv / Paper / Video / Github

We propose an active implicit object reconstruction method leveraging direct uncertainty evaluation from an implicit occupancy map and next-best-view optimization. It directly optimizes a virtual camera trajectory in the uncertainty field to maximize information gain for the current reconstruction.

Efficient Implicit Neural Reconstruction Using LiDAR
Dongyu Yan, Xiaoyang Lyu, Jieqi Shi, Yi Lin.
IEEE International Conference on Robotics and Automation (ICRA 2023)
Arxiv / Paper / Video / Github / Project Page

We propose an implicit reconstruction method that uses LiDAR scans as input, which is efficient, accurate, and applicable in various scenarios. This method can be applied to real-world scenes, even with sparse LiDAR scans and poorly aligned poses, as shown in the self-collected dataset we released.