# ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

> [ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection](https://arxiv.org/abs/2106.01178)

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## Abstract

In this paper, we introduce the task of multi-view RGB-based 3D object detection as an end-to-end optimization problem. To address this problem, we propose ImVoxelNet, a novel fully convolutional method of 3D object detection based on posed monocular or multi-view RGB images. The number of monocular images in each multiview input can variate during training and inference; actually, this number might be unique for each multi-view input. ImVoxelNet successfully handles both indoor and outdoor scenes, which makes it general-purpose. Specifically, it achieves state-of-the-art results in car detection on KITTI (monocular) and nuScenes (multi-view) benchmarks among all methods that accept RGB images. Moreover, it surpasses existing RGB-based 3D object detection methods on the SUN RGB-D dataset. On ScanNet, ImVoxelNet sets a new benchmark for multi-view 3D object detection.

<div align=center>
<img src="https://user-images.githubusercontent.com/36950400/143871445-38a55168-b8cd-4520-8ed6-f5c8c8ea304a.png" width="800"/>
</div>

## Introduction

We implement a monocular 3D detector ImVoxelNet and provide its results and checkpoints on KITTI dataset.
Results for SUN RGB-D, ScanNet and nuScenes are currently available in ImVoxelNet authors
[repo](https://github.com/saic-vul/imvoxelnet) (based on mmdetection3d).

## Results and models

### KITTI

|                    Backbone                    | Class | Lr schd | Mem (GB) | Inf time (fps) |  mAP  |                                                                                                                                                               Download                                                                                                                                                               |
| :--------------------------------------------: | :---: | :-----: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [ResNet-50](./imvoxelnet_8xb4_kitti-3d-car.py) |  Car  |   3x    |          |                | 17.26 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvoxelnet/imvoxelnet_4x8_kitti-3d-car/imvoxelnet_4x8_kitti-3d-car_20210830_003014-3d0ffdf4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvoxelnet/imvoxelnet_4x8_kitti-3d-car/imvoxelnet_4x8_kitti-3d-car_20210830_003014.log.json) |

### SUN RGB-D

|                      Backbone                       | Lr schd | Mem (GB) | Inf time (fps) | mAP@0.25 | mAP@0.5 |                                                                                                                                                                           Download                                                                                                                                                                           |
| :-------------------------------------------------: | :-----: | :------: | :------------: | :------: | :-----: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [ResNet-50](./imvoxelnet_4x2_sunrgbd-3d-10class.py) |   2x    |   7.2    |      22.5      |  40.96   |  13.50  | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvoxelnet/imvoxelnet_4x2_sunrgbd-3d-10class/imvoxelnet_4x2_sunrgbd-3d-10class_20220809_184416-29ca7d2e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvoxelnet/imvoxelnet_4x2_sunrgbd-3d-10class/imvoxelnet_4x2_sunrgbd-3d-10class_20220809_184416.log.json) |

## Citation

```latex
@article{rukhovich2021imvoxelnet,
  title={ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection},
  author={Danila Rukhovich, Anna Vorontsova, Anton Konushin},
  journal={arXiv preprint arXiv:2106.01178},
  year={2021}
}
```
