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Researcher profile

Chengzhi Wu

Chengzhi Wu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.mes-hallcond-mat.mtrl-sciRobotics

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Published work

6 published item(s)

preprint2026arXiv

EgoExoMem: Cross-View Memory Reasoning over Synchronized Egocentric and Exocentric Videos

Egocentric memory is widely used in embodied intelligence, but it may be insufficient for comprehensive spatial-temporal reasoning. Inspired by human recall from both field and observer perspectives, we introduce EgoExoMem, the first benchmark for cross-view memory reasoning over synchronized egocentric and exocentric videos. EgoExoMem contains $2.6K$ high-quality MCQs across eight temporal, spatial, and cross-view QA types. To support dual-view retrieval, we propose E$^2$-Select, a training-free frame selection method for synchronized ego-exo videos. It combines relevance-based budget allocation with per-view k-DPP sampling to handle view asymmetry and cross-view temporal consistency. Experiments show that ego and exo views provide complementary memory cues, while existing MLLMs remain far from solving the benchmark: the best model reaches only $55.3\%$. E$^2$-Select achieves state-of-the-art performance of $58.2\%$ over frame-selection and RAG-based memory baselines. Further analysis reveals systematic view-preference conflicts between question framing and answer grounding, underscoring the novelty and challenge of cross-view memory reasoning.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Two-dimensional Intrinsic Janus Structures: Design Principle and Anomalous Nonlinear Optics

Two-dimensional Janus structures have garnered rapidly growing attention across multidisciplinary fields. However, despite extensive theoretical and experimental efforts, a principle for designing intrinsic Janus materials remains elusive. Here, we propose a first-principles alloy theory based on cluster expansion, incorporating a strong repulsive interaction of a cation-mediated anion-pair cluster and refined short-range cluster-cluster competitions, to unravel the formation mechanism of intrinsic Janus structures with a distorted 1T phase among numerous competing phases. Our theory not only explains why intrinsic Janus structures are accidentally observed in RhSeCl and BiTeI which are composed of alloyed elements from different groups, but also accurately predicts a wide range of 1T-like intrinsic Janus materials that are ready for synthesis. Intriguingly, as demonstrated in the case of RhSeCl, we reveal that intrinsic Janus materials can exhibit anomalous second-harmonic generation (SHG) with a distinct quantum geometric effect, originating from strong lattice and chemical-potential mirror asymmetry. Furthermore, a novel skin effect unexpectedly emerges in finite-thickness RhSeCl, accompanied by a hidden SHG effect within the bulk region. Our theory paves the way for the ab initio design of intrinsic Janus materials, significantly accelerating progress in Janus science.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motors

To enable automatic disassembly of different product types with uncertain conditions and degrees of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed. Machine learning algorithms can be employed due to their generalization capabilities of learning from various types and variants of products. However, in reality, datasets with a diversity of samples that can be used to train models are difficult to obtain in the initial period. This may cause bad performances when the system tries to adapt to new unseen input data in the future. In order to generate large datasets for different learning purposes, in our project, we present a Blender add-on named MotorFactory to generate customized mesh models of various motor instances. MotorFactory allows to create mesh models which, complemented with additional add-ons, can be further used to create synthetic RGB images, depth images, normal images, segmentation ground truth masks, and 3D point cloud datasets with point-wise semantic labels. The created synthetic datasets may be used for various tasks including motor type classification, object detection for decentralized material transfer tasks, part segmentation for disassembly and handling tasks, or even reinforcement learning-based robotics control or view-planning.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding

We present an improved approach for 3D object detection in point cloud data based on the Frustum PointNet (F-PointNet). Compared to the original F-PointNet, our newly proposed method considers the point neighborhood when computing point features. The newly introduced local neighborhood embedding operation mimics the convolutional operations in 2D neural networks. Thus features of each point are not only computed with the features of its own or of the whole point cloud but also computed especially with respect to the features of its neighbors. Experiments show that our proposed method achieves better performance than the F-Pointnet baseline on 3D object detection tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly

On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossible. A common strategy for solving this problem is to apply simulation-to-reality (sim2real) approaches with the help of simulated scenes. While the majority of current robotics vision sim2real work focuses on image data, we present an industrial application case that uses sim2real transfer learning for point cloud data. We provide insights on how to generate and process synthetic point cloud data in order to achieve better performance when the learned model is transferred to real-world data. The issue of imbalanced learning is investigated using multiple strategies. A novel patch-based attention network is proposed additionally to tackle this problem.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-task Learning

In this paper, we develop a novel benchmark suite including both a 2D synthetic image dataset and a 3D synthetic point cloud dataset. Our work is a sub-task in the framework of a remanufacturing project, in which small electric motors are used as fundamental objects. Apart from the given detection, classification, and segmentation annotations, the key objects also have multiple learnable attributes with ground truth provided. This benchmark can be used for computer vision tasks including 2D/3D detection, classification, segmentation, and multi-attribute learning. It is worth mentioning that most attributes of the motors are quantified as continuously variable rather than binary, which makes our benchmark well-suited for the less explored regression tasks. In addition, appropriate evaluation metrics are adopted or developed for each task and promising baseline results are provided. We hope this benchmark can stimulate more research efforts on the sub-domain of object attribute learning and multi-task learning in the future.

0 citations0 reviewsNo code linkedOpen access