Yonghoon Ji

Yusheng Wang, Yonghoon Ji, Hiroshi Tsuchiya et al.

Retrieving the missing dimension information in acoustic images from 2D forward-looking sonar is a well-known problem in the field of underwater robotics. There are works attempting to retrieve 3D information from a single image which allows the robot to generate 3D maps with fly-through motion. However, owing to the unique image formulation principle, estimating 3D information from a single image faces severe ambiguity problems. Classical methods of multi-view stereo can avoid the ambiguity problems, but may require a large number of viewpoints to generate an accurate model. In this work, we propose a novel learning-based multi-view stereo method to estimate 3D information. To better utilize the information from multiple frames, an elevation plane sweeping method is proposed to generate the depth-azimuth-elevation cost volume. The volume after regularization can be considered as a probabilistic volumetric representation of the target. Instead of performing regression on the elevation angles, we use pseudo front depth from the cost volume to represent the 3D information which can avoid the 2D-3D problem in acoustic imaging. High-accuracy results can be generated with only two or three images. Synthetic datasets were generated to simulate various underwater targets. We also built the first real dataset with accurate ground truth in a large scale water tank. Experimental results demonstrate the superiority of our method, compared to other state-of-the-art methods.

Yusheng Wang, Yonghoon Ji, Chujie Wu et al.

2D forward-looking sonar is a crucial sensor for underwater robotic perception. A well-known problem in this field is estimating missing information in the elevation direction during sonar imaging. There are demands to estimate 3D information per image for 3D mapping and robot navigation during fly-through missions. Recent learning-based methods have demonstrated their strengths, but there are still drawbacks. Supervised learning methods have achieved high-quality results but may require further efforts to acquire 3D ground-truth labels. The existing self-supervised method requires pretraining using synthetic images with 3D supervision. This study aims to realize stable self-supervised learning of elevation angle estimation without pretraining using synthetic images. Failures during self-supervised learning may be caused by motion degeneracy problems. We first analyze the motion field of 2D forward-looking sonar, which is related to the main supervision signal. We utilize a modern learning framework and prove that if the training dataset is built with effective motions, the network can be trained in a self-supervised manner without the knowledge of synthetic data. Both simulation and real experiments validate the proposed method.

Guijin Son, Seungone Kim, Catherine Arnett et al.

Following the recent achievement of gold-medal performance on the IMO by frontier LLMs, the community is searching for the next meaningful and challenging target for measuring LLM reasoning. Whereas olympiad-style problems measure step-by-step reasoning alone, research-level problems use such reasoning to advance the frontier of mathematical knowledge itself, emerging as a compelling alternative. Yet research-level math benchmarks remain scarce because such problems are difficult to source (e.g., Riemann Bench and FrontierMath-Tier 4 contain 25 and 50 problems, respectively). To support reliable evaluation of next-generation frontier models, we introduce Soohak, a 439-problem benchmark newly authored from scratch by 64 mathematicians. Soohak comprises two subsets. On the Challenge subset, frontier models including Gemini-3-Pro, GPT-5, and Claude-Opus-4.5 reach 30.4%, 26.4%, and 10.4% respectively, leaving substantial headroom, while leading open-weight models such as Qwen3-235B, GPT-OSS-120B, and Kimi-2.5 remain below 15%. Notably, beyond standard problem solving, Soohak introduces a refusal subset that probes a capability intrinsic to research mathematics: recognizing ill-posed problems and pausing rather than producing confident but unjustified answers. On this subset, no model exceeds 50%, identifying refusal as a new optimization target that current models do not directly address. To prevent contamination, the dataset will be publicly released in late 2026, with model evaluations available upon request in the interim.

Shinya Matsubara, Akihiko Honda, Yonghoon Ji et al.

This paper proposes a process that uses two cameras to obtain three-dimensional (3D) information of a target object for human tracking. Results of human detection and tracking from two cameras are integrated to obtain the 3D information. OpenPose is used for human detection. In the case of a general processing a stereo camera, a range image of the entire scene is acquired as precisely as possible, and then the range image is processed. However, there are problems such as incorrect matching and computational cost for the calibration process. A new stereo vision framework is proposed to cope with the problems. The effectiveness of the proposed framework and the method is verified through target-tracking experiments.

Ryuki Suzuki, Ryosuke Kataoka, Yonghoon Ji et al.

This paper describes a novel SLAM (simultaneous localization and mapping) scheme based on scan matching in an environment including various physical properties.

Masahiro Takahashi, Alessandro Moro, Yonghoon Ji et al.

This paper aims at constructing a light-weight object detector that inputs a depth and a color image from a stereo camera. Specifically, by extending the network architecture of YOLOv3 to 3D in the middle, it is possible to output in the depth direction. In addition, Intersection over Uninon (IoU) in 3D space is introduced to confirm the accuracy of region extraction results. In the field of deep learning, object detectors that use distance information as input are actively studied for utilizing automated driving. However, the conventional detector has a large network structure, and the real-time property is impaired. The effectiveness of the detector constructed as described above is verified using datasets. As a result of this experiment, the proposed model is able to output 3D bounding boxes and detect people whose part of the body is hidden. Further, the processing speed of the model is 44.35 fps.

Satoshi Hashimoto, Yonghoon Ji, Kenichi Kudo et al.

This paper proposes an anomaly detection method for the prevention of industrial accidents using machine learning technology.