#3D Object Detection

SparseBEV is an advanced project focusing on sparse 3D object detection using multi-camera footage, designed for high efficiency and accuracy. The official PyTorch implementation, recognized at ICCV 2023, showcases top-tier performance on the nuScenes dataset. Its unique sparse sampling method boosts processing efficiency without sacrificing accuracy. The project also offers a model zoo for diverse benchmarking needs, and features like SparseOcc and visualization tools make it highly beneficial for developers and researchers.

Discover a broad array of resources on 3D Gaussian Splatting, featuring progress in areas such as 3D object detection and autonomous driving. This compilation provides access to cutting-edge methods, open-source projects, and educational materials, reflecting current research developments. Contributions are welcomed to further enrich this knowledge base. Engage with the community through blog articles and multimedia content, while staying updated on emerging research and projects.

This article offers a detailed examination of vision-centric bird's-eye-view (BEV) perception technologies relevant to autonomous driving. It discusses geometry-based, depth-based, and network-based approaches, including multi-layer perceptron (MLP) and transformer-based methods, and their use in object detection and semantic segmentation. The survey includes discussions on datasets, benchmarking outcomes, and historical technological developments. It also addresses multi-task learning and fusion strategies, emphasizing advances in multi-modality fusion for improved 3D object detection, providing a valuable insight into current BEV perception technologies.

Det3D is a 3D Object Detection toolbox providing ready-to-use implementations of algorithms like PointPillars and SECOND. It supports benchmarks such as KITTI and nuScenes and includes features like multi-dataset support, a variety of models, distributed training, SyncBN, TensorboardX, and flexible anchor dimensions, thus supporting research in 3D object detection.

FlatFormer enhances 3D point cloud transformer efficiency with flattened window attention, solving latency challenges for applications like autonomous driving. It reduces processing overhead by grouping point clouds equally and applying self-attention, achieving significant speed improvements over SST and CenterPoint while ensuring high accuracy on the Waymo Dataset. It delivers real-time performance on edge GPUs, surpassing traditional sparse convolutional methods in speed and performance on large-scale benchmarks.