Awesome GitHub Repositories3D

Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration. The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati

Grounded-Segment-Anything is a suite of specialized tools for multimodal visual analysis, text-based segmentation, and generative image editing. It integrates text-to-bounding-box detection and high-precision image segmentation masks to function as a text-based image segmenter and an automated visual labeling tool. The project enables text-driven image editing by identifying objects through natural language to perform inpainting and element replacement. It further extends visual analysis into three dimensions, allowing for 3D human reconstruction and the generation of 3D bounding boxes from t

CenterNet 是一个中心点目标检测框架和实时计算机视觉流水线。它通过预测中心点而不是使用锚框 (Anchor Boxes) 来识别对象和姿态。 该系统作为 3D 边界框估计器、人体姿态估计模型以及实时目标检测工具运行。它将关节的放置和对象位置视为中心点检测问题，以定位图像和三维空间中的实体。 能力涵盖 3D 目标检测、人体关键点估计和实时视频分析。该流水线使用单阶段前馈推理过程，对摄像头或视频文件进行持续分析。

Espectre is an edge machine learning framework and motion detection platform that uses Wi-Fi Channel State Information to identify human presence and movement. It functions as a sensing toolkit for ESP32 microcontrollers, enabling the detection of motion through walls without the use of cameras or wearables. The project distinguishes itself by executing compact neural network classifiers and mathematical detection algorithms directly on the microcontroller. It utilizes a MicroPython runtime to allow for the prototyping and deployment of sensing logic and wireless signal processing algorithms

BEVFormer 是一个感知框架，将多摄像头图像转换为自动驾驶的鸟瞰图 (BEV) 表示。它作为一个多摄像头视觉管道，将多个摄像头流集成到单个统一的空间视角中，以促进环境理解。 该系统实现了基于 Transformer 的架构，采用基于查询的特征提取和时空网络来聚合空间图像特征和时间历史数据。它使用循环时间累积来在连续帧之间保持场景的持久记忆。 该框架提供了 3D 目标检测和语义地图分割的能力。它结合了多视图图像融合与卷积检测头，以识别三维对象并将环境数据划分为有意义的语义区域。