The rapidly growing number of product categories in large-scale e-commerce makes accurate object identification for automated packing in warehouses substantially more difficult. As the catalog grows, intra-class variability and a long tail of rare or visually similar items increase, and when combined with diverse packaging, cluttered containers, frequent occlusion, and large viewpoint changes-these factors amplify discrepancies between query and reference images, causing sharp performance drops for methods that rely solely on 2D appearance features. Thus, we propose RoboEye, a two-stage identification framework that dynamically augments 2D semantic features with domain-adapted 3D reasoning and lightweight adapters to bridge training deployment gaps. In the first stage, we train a large vision model to extract 2D features for generating candidate rankings. A lightweight 3D-feature-awareness module then estimates 3D feature quality and predicts whether 3D re-ranking is necessary, preventing performance degradation and avoiding unnecessary computation. When invoked, the second stage uses our robot 3D retrieval transformer, comprising a 3D feature extractor that produces geometry-aware dense features and a keypoint-based matcher that computes keypoint-correspondence confidences between query and reference images instead of conventional cosine-similarity scoring. Experiments show that RoboEye improves Recall@1 by 7.1% over the prior state of the art (RoboLLM). Moreover, RoboEye operates using only RGB images, avoiding reliance on explicit 3D inputs and reducing deployment costs. The code used in this paper is publicly available at: https://github.com/longkukuhi/RoboEye.

A critical question that arises after identifying the objects (or class labels) in an imagery database is: "How are the various objects discovered in an imagery database correlated with one another?" This article tries to answer this question by providing a generic framework that can facilitate the readers to discover hidden correlations between objects in the imagery database. The portion of this article is drawn from our work published in IEEE BIGDATA 2021 [1].) The framework to discover the correlation between the objects in an imagery database is shown in Figure 1. Demonstration: In this demo, we first pass the image data into a trained model (e.g., resnet50) and extract objects and their scores.

SEEKER-30 AI-TIR supports dual sensors object identification and tracking based on deep learning algorithm and ECO tracking algorithm. It has an AI object identification and tracking module, with which SEEKER-30 AI-TIR can realize car, human automatic recognition and tracking by choosing the corresponding tracking mode. SEEKER-30 AI-TIR can be controlled via sbus, serial port. Functions like target tracking or pseudo-color pattern switching can be realized via sbus control. SEEKER-30 AI-TIR supports Max.128G storage.