SAS: Segment Any 3D Scene with Integrated 2D Priors

The open vocabulary capability of 3D models is increasingly valued, as traditional methods with models trained with fixed categories fail to recognize unseen objects in complex dynamic 3D scenes. In this paper, we propose a simple yet effective approach, SAS, to integrate the open vocabulary capability of multiple 2D models and migrate it to 3D domain. Specifically, we first propose Model Alignment via Text to map different 2D models into the same embedding space using text as a bridge. Then, we propose Annotation-Free Model Capability Construction to explicitly quantify the 2D model's capability of recognizing different categories using diffusion models. Following this, point cloud features from different 2D models are fused with the guide of constructed model capabilities. Finally, the integrated 2D open vocabulary capability is transferred to 3D domain through feature distillation. SAS outperforms previous methods by a large margin across multiple datasets, including ScanNet v2, Matterport3D, and nuScenes, while its generalizability is further validated on downstream tasks, e.g., gaussian segmentation and instance segmentation.

A Message Passing Detection based Affine Frequency Division Multiplexing Communication System

The new generation of wireless communication technology is expected to solve the reliability problem of communication in high-speed mobile communication scenarios. An orthogonal time frequency space (OTFS) system has been proposed and can effectively solve this problem. However, the pilot overhead and multiuser multiplexing overhead of the OTFS are relatively high. Therefore, a new modulation technology based on the discrete affine Fourier transform was proposed recently to address the above issues in OTFS, referred to the affine frequency division multiplexing (AFDM). The AFDM attains full diversity due to parameter adjustment according to the delay-Doppler profile of the channel and can achieve performance similar to the OTFS. Due to the limited research on the detection of AFDM currently, we propose a low-complexity yet efficient message passing (MP) algorithm for joint interference cancellation and detection, which takes advantage of the inherent channel sparsity. According to simulation results, the MP detection performs better than the minimum mean square error and maximal ratio combining detection.