Virtual Sparse Convolution for Multimodal 3D Object Detection (2303.02314v1)

Abstract: Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points generated from an image are very dense, introducing a huge amount of redundant computation during detection. Meanwhile, noises brought by inaccurate depth completion significantly degrade detection precision. This paper proposes a fast yet effective backbone, termed VirConvNet, based on a new operator VirConv (Virtual Sparse Convolution), for virtual-point-based 3D object detection. VirConv consists of two key designs: (1) StVD (Stochastic Voxel Discard) and (2) NRConv (Noise-Resistant Submanifold Convolution). StVD alleviates the computation problem by discarding large amounts of nearby redundant voxels. NRConv tackles the noise problem by encoding voxel features in both 2D image and 3D LiDAR space. By integrating VirConv, we first develop an efficient pipeline VirConv-L based on an early fusion design. Then, we build a high-precision pipeline VirConv-T based on a transformed refinement scheme. Finally, we develop a semi-supervised pipeline VirConv-S based on a pseudo-label framework. On the KITTI car 3D detection test leaderboard, our VirConv-L achieves 85% AP with a fast running speed of 56ms. Our VirConv-T and VirConv-S attains a high-precision of 86.3% and 87.2% AP, and currently rank 2nd and 1st, respectively. The code is available at https://github.com/hailanyi/VirConv.

• The paper introduces VirConv, a novel operator that fuses RGB and LiDAR data for precise 3D object detection.
• The paper employs Stochastic Voxel Discard and Noise-Resistant Submanifold Convolution to mitigate computational overhead and noise.
• The paper demonstrates notable gains on the KITTI dataset, achieving up to 87.2% AP with inference speeds as low as 56 ms.

An Expert Overview of "Virtual Sparse Convolution for Multimodal 3D Object Detection"

The paper "Virtual Sparse Convolution for Multimodal 3D Object Detection" introduces VirConvNet, a neural network architecture specifically designed for 3D object detection by effectively integrating RGB images and LiDAR data. The focus is on addressing computational inefficiencies and precision challenges associated with the usage of virtual or pseudo-points generated through depth completion from RGB images. The authors propose a novel operator called VirConv, constituting two main components: Stochastic Voxel Discard (StVD) and Noise-Resistant Submanifold Convolution (NRConv).

Key Concepts and Methodology

1. Virtual Points and Associated Challenges: Virtual or pseudo-points, which are generated from RGB images, attempt to enhance the spatial resolution beyond what sparse LiDAR points can offer. However, these virtual points introduce dense computationally redundant data and are prone to noise due to the inaccuracies in depth estimation.
2. VirConv Operator: The VirConv operator features:
   • Stochastic Voxel Discard (StVD): This component addresses the issue of computational overhead by discarding redundant nearby voxels, while preserving those necessary for accurately representing faraway objects. It effectively reduces unnecessary computational loads and simulates sparser training samples to enhance model robustness.
   • Noise-Resistant Submanifold Convolution (NRConv): This convolutional layer adapts the sparse voxel convolution to suppress noise-induced errors by encoding features in both the original 3D space and the 2D image space, thus leveraging spatial information in a complementary manner.
3. Network Pipelines: The researchers devised three distinct pipelines to highlight the flexibility and effectiveness of the VirConv operator:
   • VirConv-L focuses on efficiency, facilitating rapid computation with competitive precision.
   • VirConv-T targets high accuracy through a transformed refinement scheme, combining multiple-stage refinement with multi-transformation robustness.
   • VirConv-S explores semi-supervised learning to exploit unlabeled data using a pseudo-labeling framework.

Empirical Results and Implications

The paper presents comprehensive evaluations on the KITTI dataset, revealing substantial improvements over existing benchmarks. Notably, VirConv-T and VirConv-S achieve top entries on the KITTI 3D detection leaderboard, with average precision scores reaching 86.3% and 87.2% respectively. The VirConv-L model operates at impressive speeds, delivering results in 56 ms while maintaining competitive accuracy levels. These results underscore VirConvNet's promise for real-world applications like autonomous driving, where integration of multimodal sensor data is crucial.

Future Prospects

The paper's findings contribute to both practical applications and theoretical advancements in AI-driven 3D object detection. The integration of StVD and NRConv permits significant real-time application potential, particularly in computational environments where resource optimization is critical. The semi-supervised approach in VirConv-S opens avenues for leveraging vast unlabeled datasets, which is an increasingly important challenge in AI. Future explorations could involve extending the framework's modular adaptations to other multimodal datasets and refining the noise handling capabilities to further enhance autonomous systems' performance.

In conclusion, the paper meticulously constructs a novel backbone network that overcomes typical barriers faced in multimodal 3D object detection, offering a significant leap in both accuracy and computational efficiency. The methodologies and architectures proposed herein hold potential for widespread implications in AI fields requiring robust integration of multimodal sensor data.