TR3D: Towards Real-Time Indoor 3D Object Detection
Abstract
Recently, sparse 3D convolutions have changed 3D object detection. Performing on par with the voting-based approaches, 3D CNNs are memory-efficient and scale to large scenes better. However, there is still room for improvement. With a conscious, practice-oriented approach to problem-solving, we analyze the performance of such methods and localize the weaknesses. Applying modifications that resolve the found issues one by one, we end up with TR3D: a fast fully-convolutional 3D object detection model trained end-to-end, that achieves state-of-the-art results on the standard benchmarks, ScanNet v2, SUN RGB-D, and S3DIS. Moreover, to take advantage of both point cloud and RGB inputs, we introduce an early fusion of 2D and 3D features. We employ our fusion module to make conventional 3D object detection methods multimodal and demonstrate an impressive boost in performance. Our model with early feature fusion, which we refer to as TR3D+FF, outperforms existing 3D object detection approaches on the SUN RGB-D dataset. Overall, besides being accurate, both TR3D and TR3D+FF models are lightweight, memory-efficient, and fast, thereby marking another milestone on the way toward real-time 3D object detection.
Usage
Training and inference in this project were tested with mmdet3d==1.1.0rc3.
Training commands
In MMDet3D's root directory, run the following command to train the model:
python tools/train.py projects/TR3D/configs/tr3d_1xb16_scannet-3d-18class.py
Testing commands
In MMDet3D's root directory, run the following command to test the model:
python tools/test.py projects/TR3D/configs/tr3d_1xb16_scannet-3d-18class.py ${CHECKPOINT_PATH}
Results and models
ScanNet
| Backbone | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
|---|---|---|---|---|---|
| MinkResNet34 | 8.6 | 23.7 | 72.9 (72.0) | 59.3 (57.4) | model | log |
SUN RGB-D
| Backbone | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
|---|---|---|---|---|---|
| MinkResNet34 | 3.8 | 27.5 | 67.1 (66.3) | 50.4 (49.6) | model | log |
S3DIS
| Backbone | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
|---|---|---|---|---|---|
| MinkResNet34 | 15.2 | 21.0 | 74.5 (72.1) | 51.7 (47.6) | model | log |
Note
- We report the results across 5 train runs followed by 5 test runs. Median values are in round brackets.
- Inference time is given for a single NVidia GeForce RTX 4090 GPU.
Citation
@article{rukhovich2023tr3d,
title={TR3D: Towards Real-Time Indoor 3D Object Detection},
author={Rukhovich, Danila and Vorontsova, Anna and Konushin, Anton},
journal={arXiv preprint arXiv:2302.02858},
year={2023}
}
Checklist
Milestone 1: PR-ready, and acceptable to be one of the
projects/.Finish the code
Basic docstrings & proper citation
Test-time correctness
A full README
Milestone 2: Indicates a successful model implementation.
- Training-time correctness
Milestone 3: Good to be a part of our core package!
Type hints and docstrings
Unit tests
Code polishing
Metafile.yml
Move your modules into the core package following the codebase's file hierarchy structure.
Refactor your modules into the core package following the codebase's file hierarchy structure.