Unordered grasping in industrial robotic manipulation requires precise six-degree-of-freedom (6D) pose estimation. However, existing methods often struggle with unknown objects and require retraining, limiting their practicality. Traditional 3D point-pair feature methods, while training-free, perform poorly with textured symmetric objects. We propose a generalizable approach for zero-shot 6 D pose estimation without retraining. Our method consists of two steps: generating CAD-based templates through real-time rendering for coarse pose estimation, and refining poses using semantic point-pair features aligned with the camera viewpoint. We conducted experiments on seven core datasets from the Benchmark for 6D Object Pose Estimation (BOP) challenge, and the results are publicly available on the BOP website. Integration into a robotic grasping system further highlights its high precision and fast execution, making it ideal for applications such as bin-picking. (GZS6D-BP) https://bop.felk.cvut.cz/leaderboards/.

Neural Radiance Fields (NeRF) has made significant advancements in view synthesis and 3D reconstruction. However, the reconstructed outputs often lack semantic information, limiting its applicability for scene understanding tasks. Additionally, NeRF-based implicit representations focus on novel view synthesis, leading to coarse, non-editable surface meshes. This paper addresses the asset generation task within NeRF, incorporating prior semantic information to guide the reconstruction process. We propose a texture mesh recovery method that combines learned segmentation priors with NeRF. By leveraging the Segmentation Anything Model (SAM) for extracting object masks from RGB images, we ensure semantic consistency. Camera poses, derived from a multi-view reconstruction algorithm, are integrated into a mesh-based NeRF framework to learn explicit scene representations. Using an optimization approach, we disentangle texture and mesh, thereby enabling the creation of editable 3D assets. Experimental results demonstrate the effectiveness of the proposed method across multiple reconstruction datasets.

In recent years, with the continuous advancement of deep learning technology, the field of computer graphics has begun to use Neural Radiance Fields (NeRF) technology to solve traditional rendering problems. NeRF technology can output high-quality rendering results and generate three-dimensional scenes that are realistic, making it well-suited for 3D reconstruction, virtual reality, and industrial production. This article summarizes the key algorithms and important work in the NeRF field, classifies and discusses key improved models, briefly introduces the datasets and evaluation indicators in this field, and looks forward to the future development of this field, starting from the principles of NeRF technology.

Getting the point cloud data from sensors and correctly understanding the scene is the core of the intelligent transportation system. Point cloud segmentation can help intelligent transportation systems distinguish different objects in the scene. Some methods process the point cloud through a feature extraction network and complete the segmentation task. However, these methods have high requirements on the feature extraction network, and the fineness of the features will directly affect the final segmentation result. In this paper, we propose a new feature extraction network for segmentation by adding an encoder-decoder structure, which can extract the multiscale local feature information from the feature map. In our opinion, the merged multiscale features obtain a better feature matrix, which improves the performance of the segmentation tasks. We report results on the S3DIS dataset, new feature extraction network greatly improves both semantic segmentation and instance segmentation tasks.

With the continuous progress of robot grasping technology, the application of robots in industrial applications is promoted. However, reliable grasping of any object is still a difficult problem for robot grasping tasks. In this paper, the parallel grabber is studied as the grabber used in robot grabber detection. The grab detection includes the two-dimensional plane grab method and six-degree-of-freedom grab method, in which the former is constrained to grab from one direction. This paper summarizes the development trend of the two methods and analyzes their advantages and disadvantages.