News

Professor Yoon and some of his students participated in the Hanwha Aerospace presentation on December 20th and completed it smoothly. Great job, everyone!

Our Ph.D. student Yoonki Cho presented his work titled “Generalizable Person Re-identification via Balancing Alignment and Uniformity” at NeurIPS 2024 held from December 10th to December 15th, 2024. This study introduces the Balancing Alignment and Uniformity (BAU) framework to enhance domain generalization in person re-identification by maintaining a balance between alignment and uniformity, thereby improving robustness against distributional shifts. The proposed approach effectively leverages data augmentation to achieve state-of-the-art performance without requiring complex training procedures.

Professor Yoon, his Ph.D. student Yoonki Cho, and MS student Youngju Na attended the NeurIPS2024 conference held at the Vancouver Convention Center in Canada from December 10th to December 15th, 2024. There, we had the valuable opportunity to meet our doctoral student Chungsu Jang and alumnus Inyoung Cho, allowing us to engage in meaningful exchanges. Additionally, by participating in the conference, we were able to understand recent research topics and gain a broader research perspective, making it a precious opportunity.

Professor Yoon and his Ph.D. students, Jaeyoon Kim and Kyubeom Han, attended the SIGGRAPH ASIA 2024 conference held at the Tokyo International Forum in Japan from December 3rd to December 6th, 2024. The conference provided a great opportunity for the team to connect, participate in various programs, exchange ideas, and stay updated on the latest trends. Additionally, we were able to meet our alumnus, YuChi, and have catch-up conversations, offering mutual support.

On November 4th, our lab visited the Chrysanthemum Festival at Yurim Park. We strolled among the fully bloomed chrysanthemums, enjoying relaxed conversations and a refreshing break. Afterward, we shared lunch together, strengthening our team bonds. It was a meaningful outing, made even more special by the chance to connect and enjoy time with everyone in the lab.

Our PhD student SeongJoo Moon presented his work titled “LiDAR-camera Online Calibration by Representing Local Feature and Global Spatial Context” at IROS2024. This research proposes a Transformer-based framework for LiDAR-camera online calibration, addressing degradation in calibration accuracy caused by physical vibrations and environmental changes. By learning feature correspondences and integrating global spatial context, the method achieves superior calibration performance compared to existing benchmarks, enhancing perception systems for autonomous driving applications.

Our PhD student Mincheul Kim presented his work titled “CCTV-Informed Human-Aware Robot Navigation in Crowded Indoor Environments” at IROS 2024. This study, which has also been accepted for publication in RAL 2024, proposes a novel navigation framework that integrates CCTV data with deep reinforcement learning to overcome the limitations of onboard sensors, enabling robots to navigate crowded indoor spaces safely and efficiently. By leveraging external CCTV insights on human movements, the framework anticipates interactions, minimizes collisions, and generates socially acceptable paths, significantly improving navigation compared to methods reliant solely on local sensing.

Our PhD student Minsung Yoon presented his work titled “Learning-based Adaptive Control of Quadruped Robots for Active Stabilization on Moving Platforms” at IROS2024. This research introduces Learning-based Active Stabilization on Moving Platforms (LAS-MP), a novel system that enables quadruped robots to balance effectively on six-DOF moving platforms by leveraging adaptive posture adjustment policies and state estimators trained on proprioceptive sensor data. The framework systematically addresses challenges caused by platform-induced inertia forces and showcases superior performance over baselines through trajectory generation, ablation studies, and detailed evaluations of its components.

Our Ph.D. students Seongjoo Moon, Mincheul Kim, and Minsung Yoon, along with our M.S. student Jiwoo Hwang, attended the IROS2024 conference held at ADNEC in Abu Dhabi, UAE, from October 14th to October 18th, 2024. The conference offered a great opportunity for the team to connect, participate in various programs, and exchange ideas. It was a valuable experience for discussing future research directions and staying updated on the latest developments in the field.

Our lab alumnus, Prof. Bochang Moon, conducted a seminar on October 14, 2024, in the undergraduate graphics class, presenting his paper “Target-Aware Image Denoising for Inverse Monte Carlo Rendering.” This study develops a target-aware image denoiser to address the noise issue in scene parameter inference through physically based differentiable rendering. The seminar was highly beneficial for students, helping them understand the challenges in inverse rendering and learn about the latest advancements in noise reduction techniques.

Professor Yoon and his M.S. students attended the KAIA2024 conference in Busan from August 15th to 17th, 2024. They presented their research papers and explored the latest advancements in artificial intelligence.

Seokryun Choi presented his paper, “Event-based Trajectory Estimation of a Ball,” which proposes synthesizing event data in Isaac Gym by emulating the mechanism of event cameras.

Taeyeon Kim presented her paper, “Articulated Motion Retargeting with 4D Gaussian Splatting,” which addresses the limitations of motion retargeting between reconstructed objects in a template-free domain.

The conference offered valuable opportunities that significantly enriched their academic experience.

Our lab alumnus, Prof. Bochang Moon, participated in SIGGRAPH 2024, held in Denver from July 28 to August 1, and presented his research titled “Target-Aware Image Denoising for Inverse Monte Carlo Rendering.” This study addresses the noise issue that arises when inferring scene parameters through physically based differentiable rendering by developing a target-aware image denoiser. The research points out that using conventional image denoisers can lead to undesirable local minima due to denoising bias and proposes a new denoiser that incorporates target image information to overcome this limitation. By determining denoising weights through linear regression based on target images, this method enables more robust scene parameter inference in inverse rendering optimization.

Chanmi Lee presented a paper at the KCC2024 Conference in the Artificial Intelligence Applications section, held from June 26th to 28th, 2024. Her presentation focused on the research topic “Adversarial Attack on Visuomotor Policy.” This research reveals a vulnerability in vision-based visuomotor policies, particularly those trained with Soft Actor-Critic (SAC). To address this, she proposed a method to attack the policy, analyzing the robot’s movement path to evaluate how adversarial perturbations impact the robot’s decision-making process.