#3D object detection

Discover EFG, an adaptable deep learning framework offering capabilities for 3D object detection and tracking, suitable for diverse research applications with structured project templates. This framework supports advanced features, including COCO Panoptic Segmentation and Pytorch 2.0 compatibility. Recent code releases for ICCV2023 and CVPR2023 papers integrate methodologies like TrajectoryFormer and ConQueR. Optimized for extensive datasets such as Waymo and nuScenes, EFG achieves notable benchmark performances on NVIDIA A100 GPUs. It provides a versatile solution for researchers aiming for flexibility and efficiency in a progressive deep learning landscape.

BEVFusion integrates camera and LiDAR data in a bird's-eye view format, enhancing autonomous driving sensors by preserving vital semantic information and optimizing performance. It achieves superior detection results and lower latency, with proven effectiveness in top-tier benchmarks.

The Dynamic Sparse Voxel Transformer (DSVT) efficiently analyzes large-scale point clouds in autonomous driving. Excelling with state-of-the-art performance in the Waymo Open Dataset, it achieves real-time 3D object detection through unique rotated set partitioning. With minimal dependencies, DSVT optimizes performance in both single-sweep and multi-sweep configurations. Highlighted at CVPR2023 and integrated into the OpenPCDet framework, DSVT represents significant advancements in 3D perception without overstating its capabilities.

MMDetection3D is an open-source library tailored for 3D object detection, efficiently handling diverse indoor and outdoor datasets such as KITTI and nuScenes. It provides versatile detectors for seamless integration into various applications, featuring efficient training and evaluation processes. The platform offers advanced models and customizable components supporting both 3D detection and semantic segmentation, making it a valuable tool for developing modern 3D detection technologies.