Ningkai Mo — accepted AI-conference papers

2025

GaussianOcc: Fully Self-supervised and Efficient 3D Occupancy Estimation with Gaussian Splatting

Wanshui Gan, Fang Liu, Hongbin Xu, Ningkai Mo, Naoto Yokoya

ICCV 2025poster

We introduce GaussianOcc, a systematic method that investigates Gaussian splatting for fully self-supervised and efficient 3D occupancy estimation in surround views. First, traditional methods for self-supervised 3D occupancy estimation still require ground truth 6D poses from sensors during trainin…

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2022

ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework

Ningkai Mo, Wanshui Gan, Naoto Yokoya, Shifeng Chen

CVPR 2022poster

In this paper, a computation efficient regression framework is presented for estimating the 6D pose of rigid objects from a single RGB-D image, which is applicable to handling symmetric objects. This framework is designed in a simple architecture that efficiently extracts point-wise features from RG…

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