ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection (2106.01178v3)

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 monocular or multi-view RGB images. The number of monocular images in each multi-view 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. The source code and the trained models are available at https://github.com/saic-vul/imvoxelnet.

• The paper introduces ImVoxelNet, a novel end-to-end network that unifies monocular and multi-view 3D object detection using voxel projection.
• It employs 2D feature extraction and voxel volume construction, paired with specialized detection heads for both indoor and outdoor scenarios.
• Empirical evaluations on KITTI, nuScenes, and ScanNet demonstrate state-of-the-art performance, highlighting its versatility across benchmarks.

An Analysis of ImVoxelNet: Advancements in RGB-Based 3D Object Detection

The paper under consideration focuses on a crucial task in computer vision: RGB-based 3D object detection. With the introduction of ImVoxelNet, the authors provide an innovative approach that effectively handles both monocular and multi-view 3D object detection leveraging RGB images, positioning the method as an adaptable solution across various settings.

Methodology Overview

ImVoxelNet is architected as a fully convolutional network capable of accepting monocular or posed multi-view RGB images as inputs, addressing the task as an end-to-end optimization problem. The framework utilizes a voxel representation of 3D space to accumulate information across multiple image views. This design ensures general-purpose functionality, surpassing the limitations of domain-specific methods typically adopted in RGB-based 3D object detection.

The architecture consists of the following components:

1. 2D Feature Extraction: The method begins with feature extraction using a 2D convolutional backbone. This is followed by a Feature Pyramid Network (FPN) aggregation to consolidate these features into a coherent output.
2. 3D Volume Construction: The extracted 2D features are projected into a constructed voxel volume, retaining spatial information across the multi-view inputs. The authors employ element-wise averaging to integrate features from various views.
3. 3D Feature Extraction: A 3D convolutional network refines these gathered features. The architecture comprises a streamlined encoder-decoder setup optimized for performance and computational efficiency.
4. Detection Head: The methodology diverges for indoor and outdoor detection. The outdoor head translates 3D detection into a 2D task using BEV projections. Indoor settings leverage a novel 3D detection head inspired by the FCOS architecture, incorporating center sampling.

Results and Evaluation

The empirical evaluation of ImVoxelNet on prominent indoor and outdoor benchmarks, including KITTI, nuScenes, SUN RGB-D, and ScanNet, demonstrates its effectiveness. The network attains state-of-the-art results in KITTI and nuScenes for car detection and sets a new benchmark on ScanNet for multi-view detection. The reported metrics highlight significant improvements in AP, particularly in settings beyond monocular inputs.

Implications and Future Directions

ImVoxelNet exemplifies a robust unification of monocular and multi-view RGB-based 3D object detection, denoting a shift towards more versatile frameworks in computational vision. The model's capability to apply invariant head adaptations for indoor and outdoor scenarios illustrates its potential for wider applications, reducing the dependency on specialized techniques tailored to specific datasets or scenarios.

The scalability of the method, with its inherent capacity to incorporate arbitrary numbers of input views, suggests the potential for further developments in handling dynamic scenes or real-time applications involving sequential inputs. Continued exploration might focus on enhancing the computational efficiency of 3D convolutions and optimizing the model's adaptability to various camera configurations.

In conclusion, ImVoxelNet posits a significant contribution to the development of RGB-based 3D object detection methodologies. Its universal applicability, coupled with robust performance across diverse benchmarks, marks an evolving landscape in computer vision research, where methods demonstrate resilience and adaptability in varied perceptual tasks. The impact of this research may well extend into future advancements, prompting further refinements in the field of comprehensive 3D scene understanding.