# open-mmlab/mmdetection3d

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6,273 stars · 1,732 forks · Python · apache-2.0

## Links

- GitHub: https://github.com/open-mmlab/mmdetection3d
- Homepage: https://mmdetection3d.readthedocs.io/en/latest/
- awesome-repositories: https://awesome-repositories.com/repository/open-mmlab-mmdetection3d.md

## Topics

`3d-object-detection` `object-detection` `point-cloud` `pytorch`

## Description

MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding.

The project distinguishes itself through a config-driven pipeline that orchestrates the entire training, evaluation, and inference workflow, with support for distributed training across multiple GPUs and machines. It includes a registry-based module composition system for assembling custom models from encoder, backbone, neck, head, and loss components, and provides built-in support for sparse convolution acceleration using libraries like spconv and MinkowskiEngine. The toolbox also offers a unified dataset format conversion system that transforms raw sensor data from benchmarks such as KITTI, Waymo, and nuScenes into a standardized internal structure, along with checkpoint-based training resumption and mixed precision training for fault-tolerant and efficient workflows.

Beyond its core detection and segmentation capabilities, the project provides a comprehensive set of tools for data preparation, augmentation, and evaluation. It includes data structuring for LiDAR, multi-modal, and vision-based detection tasks, point cloud augmentation techniques, and dataset-specific evaluation protocols with metrics like mean Average Precision. The toolbox also supports model deployment, leaderboard submission for autonomous driving benchmarks, and integration with over 500 pre-trained 2D detection models from a shared codebase. Installation is available via pip or the MIM tool, and the project can be run in Docker containers or on Windows for cross-platform compatibility.

## Tags

### Artificial Intelligence & ML

- [Custom Registrations](https://awesome-repositories.com/f/artificial-intelligence-ml/backbone-integrations/custom-registrations.md) — Register a new backbone network and integrate it into the model pipeline for extracting feature maps. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html))
- [Bounding Box Representations](https://awesome-repositories.com/f/artificial-intelligence-ml/bounding-box-regression/bounding-box-representations.md) — Stores and manipulates 3D bounding boxes with position, dimensions, yaw rotation, and coordinate system conversions. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/coord_sys_tutorial.html))
- [Detection Head Registrations](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-models/classifier-head-management/auxiliary-classification-heads/detection-head-registrations.md) — Register a new bbox or RoI head and integrate it into the model pipeline for predicting object locations and classes. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/kitti.html))
- [Training Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/cross-hardware-model-inference/training-execution.md) — Starts training jobs for 3D detection models using prepared configurations and datasets. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/new_data_model.html))
- [3D Point Cloud Custom Training](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/3d-point-cloud-custom-training.md) — Converts raw point cloud data from custom sensors into structured formats for training 3D detection models. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_dataset.html))
- [Sensor Data Preparations](https://awesome-repositories.com/f/artificial-intelligence-ml/data-preparation/sensor-data-preparations.md) — Converts raw sensor data into structured annotation and database files for model training. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog.html))
- [3D Detection Pipeline Configurations](https://awesome-repositories.com/f/artificial-intelligence-ml/hyperparameter-configurations/3d-detection-pipeline-configurations.md) — Enables adapting 3D detection pipelines to new datasets through configuration files. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Detection Loss Registrations](https://awesome-repositories.com/f/artificial-intelligence-ml/loss-function-customization/detection-loss-registrations.md) — Register a new loss function and apply it to a specific head's loss field for training the model. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [3D Detection Model Zoos](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/fine-tuning-and-customization/model-fine-tuning/pre-trained-model-zoos/3d-detection-model-zoos.md) — Run inference with pre-trained 3D detection models from a model zoo covering lidar, camera, and multi-modal architectures. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/dataset_prepare.html))
- [3D Detection Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-evaluation/3d-detection-metrics.md) — Computes standard 3D detection metrics like mAP and NDS on benchmark datasets during or after training. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/inference.html))
- [Model Component Registries](https://awesome-repositories.com/f/artificial-intelligence-ml/model-component-registries.md) — Assemble a 3D detection model by plugging in custom encoder, backbone, neck, head, RoI extractor, or loss modules through a registry. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/useful_tools.html))
- [Model Evaluation Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/model-evaluation-metrics.md) — Run a trained model on the KITTI test set and compute mean Average Precision and Average Orientation Similarity metrics. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [3D Detection Evaluations](https://awesome-repositories.com/f/artificial-intelligence-ml/model-evaluation-metrics/3d-detection-evaluations.md) — Run trained 3D detection models on test data and compute metrics using dataset-specific evaluation protocols. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [3D Detection Model Configurations](https://awesome-repositories.com/f/artificial-intelligence-ml/model-parameter-configurations/3d-detection-model-configurations.md) — Provides config-driven adjustment of 3D detection model parameters for custom datasets. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Structured Experiment Configurations](https://awesome-repositories.com/f/artificial-intelligence-ml/model-parameter-configurations/structured-experiment-configurations.md) — Provides a structured config system for managing training, testing, and model parameters. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_dataset.html))
- [Point Cloud Segmentation Executions](https://awesome-repositories.com/f/artificial-intelligence-ml/segmentation-building-blocks/segmentation-mask-definitions/point-guided-segmentation/point-cloud-segmentation-executions.md) — Runs a pre-trained segmentation model on a point cloud and saves or displays the predicted per-point class labels. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [LiDAR-Camera Fusions](https://awesome-repositories.com/f/artificial-intelligence-ml/sensor-fusion/lidar-camera-fusions.md) — Implements LiDAR-camera data fusion pipelines that synchronize point clouds and images for 3D detection. ([source](https://cdn.jsdelivr.net/gh/open-mmlab/mmdetection3d@main/README.md))
- [Custom Dataset Definitions](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-dataset-definitions.md) — Adapts the data pipeline to support custom datasets for training or evaluating 3D detection models. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Dataset Class Balancing](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-class-balancing.md) — Uses a class-balanced group sampling wrapper to handle imbalanced class distributions. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_dataset.html))
- [Dataset Registration Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-registration-systems.md) — Provides a mechanism for registering custom dataset classes to load annotation and label data. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/data_pipeline.html))
- [Detection Visualization](https://awesome-repositories.com/f/artificial-intelligence-ml/face-detection/detection-visualization.md) — Renders predicted 3D bounding boxes on images for qualitative inspection of model outputs. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Large-Scale Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-model-training.md) — Distributes training across several machines or GPUs using Slurm or direct ethernet connections. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Learning Rate Decay Schedules](https://awesome-repositories.com/f/artificial-intelligence-ml/learning-rate-decay-schedules.md) — Supports configuring learning rate decay schedules like step, cosine annealing, and polynomial decay. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/new_data_model.html))
- [Mixed Precision Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/mixed-precision-training.md) — Accelerates training and reduces memory usage by performing operations in half-precision floating point. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/useful_tools.html))
- [Distributed Executions](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/mixed-precision-training/distributed-executions.md) — Scales model training across multiple GPUs and machines with optional half-precision floating point for faster throughput.
- [Waymo Detection Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/model-evaluation-metrics/waymo-detection-metrics.md) — Computes standard Waymo detection metrics by building and running the official evaluation binary. ([source](https://mmdetection3d.readthedocs.io/en/latest/model_zoo.html))
- [Training Runtime Hook Configurations](https://awesome-repositories.com/f/artificial-intelligence-ml/model-hook-configurations/training-runtime-hook-configurations.md) — Configures built-in hooks for logging, checkpoint saving, and visualization during training. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [2D Model Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/model-integration-interfaces/detection/2d-model-integrations.md) — Integrates over 500 pre-trained 2D detection models from a shared codebase alongside 3D detection workflows. ([source](https://cdn.jsdelivr.net/gh/open-mmlab/mmdetection3d@main/README.md))
- [Model Performance Evaluators](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-evaluators.md) — Computes mean intersection over union across semantic classes to measure model performance. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/useful_tools.html))
- [nuScenes Leaderboard Submissions](https://awesome-repositories.com/f/artificial-intelligence-ml/model-performance-visualizations/model-performance-leaderboards/kitti-3d-detection-leaderboard-submissions/nuscenes-leaderboard-submissions.md) — Produce prediction files in the required format for submission to the nuScenes benchmark leaderboard. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/semantickitti.html))
- [Vision Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-frameworks/vision-model-training.md) — Trains a monocular 3D object detection model using distributed GPU training with automatic learning rate scaling. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/s3dis.html))
- [Bird's-Eye-View Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training-frameworks/vision-model-training/bird-s-eye-view-training-pipelines.md) — Loads multi-view images and applies augmentations to train bird's-eye-view 3D object detectors. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/kitti.html))
- [Gradient Clipping Configurations](https://awesome-repositories.com/f/artificial-intelligence-ml/optimizer-configurations/gradient-clipping-configurations.md) — Provides a unified wrapper for configuring optimizers, learning rates, and gradient clipping. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Pre-trained Model Application](https://awesome-repositories.com/f/artificial-intelligence-ml/pre-trained-model-application.md) — Uses a library of over 500 pre-trained models to perform inference on new point cloud or multi-modal data. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/inference.html))
- [Autonomous Driving Submissions](https://awesome-repositories.com/f/artificial-intelligence-ml/prediction-submission-tools/autonomous-driving-submissions.md) — Formats model predictions into leaderboard-compatible submission files for KITTI, Waymo, and nuScenes.
- [Test Time Augmentation](https://awesome-repositories.com/f/artificial-intelligence-ml/test-time-augmentation.md) — Applies double flip augmentation during test time for improved CenterPoint detection accuracy. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Training and Testing Splits](https://awesome-repositories.com/f/artificial-intelligence-ml/training-and-testing-splits.md) — Selects which S3DIS areas to use for training and validation by specifying area indices. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/inference.html))
- [Checkpoint Resumption](https://awesome-repositories.com/f/artificial-intelligence-ml/training-checkpointing/checkpoint-resumption.md) — Automatically resumes interrupted training from the latest saved checkpoint, preserving optimizer states and iteration progress.
- [Custom Point Cloud Dataset Training](https://awesome-repositories.com/f/artificial-intelligence-ml/training-dataset-processing/custom-point-cloud-dataset-training.md) — Provides a pipeline for training 3D detection models on custom point cloud datasets with user-provided annotations. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_dataset.html))
- [Waymo 3D Perception Dataset Processors](https://awesome-repositories.com/f/artificial-intelligence-ml/training-dataset-processing/waymo-3d-perception-dataset-processors.md) — Preprocesses, trains, tests, and evaluates models on the Waymo dataset with accelerated data handling. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog.html))
- [Training Loop Control](https://awesome-repositories.com/f/artificial-intelligence-ml/training-loop-control.md) — Supports choosing between epoch-based and iteration-based training loops with dynamic validation intervals. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))

### Part of an Awesome List

- [LiDAR Detection Data Structuring](https://awesome-repositories.com/f/awesome-lists/ai/3d-and-lidar-annotation/lidar-detection-data-structuring.md) — Organizes raw point cloud files and annotations into a prescribed folder layout for LiDAR detection. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_runtime.html))
- [3D Object Detection](https://awesome-repositories.com/f/awesome-lists/ai/3d-object-detection.md) — Trains and evaluates a variety of 3D detection architectures on point cloud and multi-modal datasets. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/supported_tasks/vision_det3d.html))
- [Monocular Detections](https://awesome-repositories.com/f/awesome-lists/ai/3d-object-detection/monocular-detections.md) — Provides monocular 3D object detection from single camera images with known calibration. ([source](https://cdn.jsdelivr.net/gh/open-mmlab/mmdetection3d@main/README.md))
- [Multi-Modal Detections](https://awesome-repositories.com/f/awesome-lists/ai/3d-object-detection/multi-modal-detections.md) — Ships multi-modal 3D detection that fuses camera images with LiDAR point clouds for improved accuracy. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Point Cloud Detections](https://awesome-repositories.com/f/awesome-lists/ai/3d-object-detection/point-cloud-detections.md) — Trains and evaluates 3D object detectors on raw LiDAR point clouds to predict oriented bounding boxes. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/new_data_model.html))
- [Inference Executions](https://awesome-repositories.com/f/awesome-lists/ai/3d-object-detection/point-cloud-detections/inference-executions.md) — Runs trained 3D detection models on new point cloud or multi-modal data to produce object predictions. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_models.html))
- [Training Executions](https://awesome-repositories.com/f/awesome-lists/ai/3d-object-detection/point-cloud-detections/training-executions.md) — Trains 3D object detection models on point cloud or multi-modal data using a provided baseline configuration. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Training Pipelines](https://awesome-repositories.com/f/awesome-lists/ai/3d-object-detection/point-cloud-detections/training-pipelines.md) — Loads point clouds from multiple sweeps and applies augmentations to train 3D object detectors on LiDAR data. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/nuscenes.html))
- [LiDAR-Based Training](https://awesome-repositories.com/f/awesome-lists/ai/model-training-and-fine-tuning/lidar-based-training.md) — Loads point clouds from multiple sweeps and applies augmentations like rotation and flipping for 3D detection. ([source](https://cdn.jsdelivr.net/gh/open-mmlab/mmdetection3d@main/README.md))
- [Point Cloud and 3D Processing](https://awesome-repositories.com/f/awesome-lists/ai/point-cloud-and-3d-processing.md) — Provides 3D semantic segmentation of point clouds for scene understanding tasks like road marking and indoor object segmentation. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [3D Point Cloud Representations](https://awesome-repositories.com/f/awesome-lists/ai/point-cloud-and-3d-processing/3d-point-cloud-representations.md) — Stores and manipulates 3D point cloud data with spatial coordinates and optional attributes like color and height. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/coord_sys_tutorial.html))
- [LiDAR Semantic Segmentation Data Structuring](https://awesome-repositories.com/f/awesome-lists/ai/semantic-segmentation/lidar-semantic-segmentation-data-structuring.md) — Organizes point cloud files and semantic masks into a prescribed layout for LiDAR segmentation. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Point Cloud Processing](https://awesome-repositories.com/f/awesome-lists/data/point-cloud-processing.md) — Loads, transforms, and augments point cloud data from multiple sweeps and sensor modalities for model input. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/kitti.html))
- [Semantic Segmentations](https://awesome-repositories.com/f/awesome-lists/data/point-cloud-processing/semantic-segmentations.md) — Assigns semantic class labels to every point in LiDAR scans and indoor point clouds for scene understanding. ([source](https://cdn.jsdelivr.net/gh/open-mmlab/mmdetection3d@main/README.md))
- [Augmentations](https://awesome-repositories.com/f/awesome-lists/data/point-cloud/augmentations.md) — Applies random noise, flipping, rotation, and scaling to point cloud data during training. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_runtime.html))
- [Vision Detection Data Structuring](https://awesome-repositories.com/f/awesome-lists/devtools/data-format-standards/computer-vision-detection-formats/vision-detection-data-structuring.md) — Organizes camera images and calibration files into a prescribed layout for vision-based detection. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Model Evaluation](https://awesome-repositories.com/f/awesome-lists/learning/model-evaluation.md) — Evaluates trained 3D detection models against standard benchmarks on datasets like KITTI, nuScenes, and Waymo. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/inference.html))
- [3D Detection Benchmarks](https://awesome-repositories.com/f/awesome-lists/learning/model-evaluation/3d-detection-benchmarks.md) — Benchmarks trained models against standard indoor and outdoor 3D detection datasets with built-in evaluation pipelines. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/inference.html))
- [Segmentation Evaluation Metrics](https://awesome-repositories.com/f/awesome-lists/ai/3d-detection-and-segmentation/segmentation-evaluation-metrics.md) — Ships evaluation pipelines that compute per-class and mean IoU for 3D segmentation models. ([source](https://cdn.jsdelivr.net/gh/open-mmlab/mmdetection3d@main/README.md))
- [Training Pipelines](https://awesome-repositories.com/f/awesome-lists/ai/3d-object-detection/monocular-detections/training-pipelines.md) — Loads single-view images with camera intrinsics and 3D annotations to train monocular 3D object detectors. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/inference.html))
- [Multi-Camera Detections](https://awesome-repositories.com/f/awesome-lists/ai/3d-object-detection/multi-modal-detections/multi-camera-detections.md) — Supports transformation for multi-camera 3D object detection. ([source](https://mmdetection3d.readthedocs.io/en/latest/model_zoo.html))
- [Sparse 3D Convolutional Networks](https://awesome-repositories.com/f/awesome-lists/ai/convolutional-neural-networks/sparse-3d-convolutional-networks.md) — Integrates the MinkowskiEngine library for sparse 3D convolutional neural networks on point clouds. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html))
- [Point-Cloud-Only Training](https://awesome-repositories.com/f/awesome-lists/ai/point-cloud-processing/point-cloud-only-training.md) — Supports training 3D detection models on datasets containing only point cloud data without image modalities. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/nuscenes.html))
- [3D Detection Benchmarks](https://awesome-repositories.com/f/awesome-lists/ai/pre-trained-models/3d-detection-benchmarks.md) — Provides baseline results for numerous 3D detection models across standard datasets. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/inference.html))
- [Format Converters](https://awesome-repositories.com/f/awesome-lists/data/point-cloud/format-converters.md) — Converts point cloud files from PCD and LAS formats into BIN format for training. ([source](https://mmdetection3d.readthedocs.io/en/latest/api.html))

### Data & Databases

- [Training Data Pipelines](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/data-processing/ml-data-pipelines/training-data-pipelines.md) — Assembles sequences of operations to load, transform, and format point cloud data for model training. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Sensor Synchronizations](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/data-processing/multi-modal-data-processors/sensor-synchronizations.md) — Loads and synchronizes point clouds, camera images, and calibration data from multiple sweeps into a unified input.
- [Dataset Preparation Scripts](https://awesome-repositories.com/f/data-databases/dataset-preparation-scripts.md) — Downloads, organizes, and preprocesses supported 3D datasets into expected folder structures and annotation formats. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/useful_tools.html))
- [Multi-Modal Detection Data Structuring](https://awesome-repositories.com/f/data-databases/multi-modal-data-management/multi-modal-detection-data-structuring.md) — Organizes point clouds, images, and calibration files into a prescribed layout for multi-modal detection. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_runtime.html))
- [Sensor Dataset Conversions](https://awesome-repositories.com/f/data-databases/structured-data-schemas/format-conversions/sensor-dataset-conversions.md) — Transforms raw sensor data from KITTI, Waymo, and nuScenes into a standardized internal structure for model consumption.
- [3D Sensor Data Format Converters](https://awesome-repositories.com/f/data-databases/3d-sensor-data-format-converters.md) — Transforms raw point cloud and image files from vendor formats into KITTI-style representations for training. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Coordinate System Mapping](https://awesome-repositories.com/f/data-databases/data-mapping/coordinate-system-mapping.md) — Transforms point coordinates, box dimensions, and yaw angles between camera, LiDAR, and depth coordinate systems. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [Data Transformation Registrations](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/processing-pipelines/pipeline-customizers/data-transformation-registrations.md) — Ships a registry for adding custom data transformation steps into processing pipelines. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_runtime.html))
- [Dataset Configuration Systems](https://awesome-repositories.com/f/data-databases/dataset-configuration-systems.md) — Sets up the data pipeline, dataloader, and evaluator in a config file to train and validate on a custom dataset. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))
- [KITTI 3D Perception Dataset Preparers](https://awesome-repositories.com/f/data-databases/dataset-preparation-scripts/kitti-3d-perception-dataset-preparers.md) — Converts raw KITTI point cloud and annotation files into structured formats for 3D detection training. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/dataset_prepare.html))
- [Format Conversions](https://awesome-repositories.com/f/data-databases/structured-data-schemas/format-conversions.md) — Converts raw point cloud and annotation data into standard formats like KITTI for training pipeline compatibility. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/kitti.html))

### Graphics & Multimedia

- [Inference Executions](https://awesome-repositories.com/f/graphics-multimedia/3d-model-visualizers/inference-executions.md) — Runs pre-trained 3D detection and segmentation models on new sensor data to generate predictions. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog.html))
- [KITTI Format Converters](https://awesome-repositories.com/f/graphics-multimedia/3d-point-cloud-spatial-mappers/point-cloud-generators/kitti-format-converters.md) — Converts raw point cloud data into KITTI-format annotations and info files for training. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_runtime.html))

### Programming Languages & Runtimes

- [Voxel Encoder Registrations](https://awesome-repositories.com/f/programming-languages-runtimes/character-encoding-utilities/custom-encoders/voxel-encoder-registrations.md) — Register a new voxel or middle encoder and integrate it into the model pipeline for processing point cloud data. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/lyft.html))

### Software Engineering & Architecture

- [Configuration-Driven Pipelines](https://awesome-repositories.com/f/software-engineering-architecture/configuration-driven-pipelines.md) — Controls the entire training, evaluation, and inference workflow through hierarchical configuration files.
- [Module-Based Registries](https://awesome-repositories.com/f/software-engineering-architecture/integration-extensibility/dependency-injection/module-based-registries.md) — Assembles 3D detection models by registering and composing custom encoder, backbone, neck, head, and loss modules.
- [Training Lifecycle Hooks](https://awesome-repositories.com/f/software-engineering-architecture/training-lifecycle-hooks.md) — Provides hooks for injecting custom logic at specific stages of the training lifecycle. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_dataset.html))

### DevOps & Infrastructure

- [Model Inference Deployment](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/model-inference-deployment.md) — Converts trained 3D detection models into deployable formats and serves them for production inference. ([source](https://mmdetection3d.readthedocs.io/en/latest/user_guides/dataset_prepare.html))

### Education & Learning Resources

- [Benchmark Submissions](https://awesome-repositories.com/f/education-learning-resources/educational-resources/algorithms-theory-academics/cs-theory-foundations/data-structure-implementations/data-structures/leaderboards/benchmark-submissions.md) — Formats and packages model predictions for upload to autonomous driving dataset leaderboards. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html))

### Scientific & Mathematical Computing

- [Sparse Voxel Operations](https://awesome-repositories.com/f/scientific-mathematical-computing/data-modeling-processing/computational-graphs/graph-based-computational-execution/deep-learning-execution/sparse-voxel-operations.md) — Accelerates 3D point cloud processing using sparse convolutional libraries like spconv and MinkowskiEngine.
- [Point Cloud Filtering](https://awesome-repositories.com/f/scientific-mathematical-computing/point-cloud-filtering.md) — Filters background points from point cloud data during preprocessing to reduce noise. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_dataset.html))
- [Sparse Convolution Libraries](https://awesome-repositories.com/f/scientific-mathematical-computing/sparse-data-processing/sparse-convolution-libraries.md) — Integrates the spconv 2.0 library for accelerated sparse convolution on point cloud data. ([source](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html))

### Testing & Quality Assurance

- [Autonomous Driving Benchmark Submissions](https://awesome-repositories.com/f/testing-quality-assurance/agent-performance-benchmarks/benchmark-result-analysis/benchmark-result-comparison/autonomous-driving-benchmark-submissions.md) — Generates prediction files and submits results to KITTI, nuScenes, and Waymo leaderboards for standardized comparison. ([source](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog_v1.0.x.html))