CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection (2009.00784v1)

Abstract: There have been significant advances in neural networks for both 3D object detection using LiDAR and 2D object detection using video. However, it has been surprisingly difficult to train networks to effectively use both modalities in a way that demonstrates gain over single-modality networks. In this paper, we propose a novel Camera-LiDAR Object Candidates (CLOCs) fusion network. CLOCs fusion provides a low-complexity multi-modal fusion framework that significantly improves the performance of single-modality detectors. CLOCs operates on the combined output candidates before Non-Maximum Suppression (NMS) of any 2D and any 3D detector, and is trained to leverage their geometric and semantic consistencies to produce more accurate final 3D and 2D detection results. Our experimental evaluation on the challenging KITTI object detection benchmark, including 3D and bird's eye view metrics, shows significant improvements, especially at long distance, over the state-of-the-art fusion based methods. At time of submission, CLOCs ranks the highest among all the fusion-based methods in the official KITTI leaderboard. We will release our code upon acceptance.

• The paper introduces a novel fusion network that merges Camera and LiDAR detection proposals to enhance 3D object detection.
• The method leverages geometric and semantic consistencies to significantly improve precision on the KITTI benchmark.
• Its modular design integrates with various pre-trained detectors, achieving top performance with minimal latency.

An Examination of CLOCs: Camera-LiDAR Object Candidates Fusion for Enhanced 3D Object Detection

The paper "CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection" presents a novel methodology for enhancing 3D object detection through a multi-modal fusion approach that combines both Camera and LiDAR data. Authored by Su Pang, Daniel Morris, and Hayder Radha from Michigan State University, the research introduces the Camera-LiDAR Object Candidates (CLOCs) fusion network, which aims to address the challenges inherent in leveraging multi-sensor data for autonomous driving systems.

Introduction and Motivation

Autonomous vehicles require robust 3D perception to accurately map and navigate their surroundings. While individual advancements in 3D object detection using LiDAR and 2D object detection using video have been noteworthy, effectively integrating these two modalities has proven to be challenging. Traditional single-modality approaches are hindered by limitations, such as LiDAR's lower resolution at longer ranges, which adversely impacts accuracy. Human annotators typically utilize both modalities to produce ground truth bounding boxes, highlighting the potential for a multi-modal fusion system that exploits both geometric and semantic consistencies.

CLOCs Fusion Network: Architecture and Contributions

The CLOCs fusion architecture is designed to operate on the combined output candidates from 2D and 3D detectors before Non-Maximum Suppression (NMS). The approach leverages geometric and semantic consistencies to improve both 3D and 2D detection accuracy. Notably, CLOCs is versatile and modular, capable of integrating various pre-trained 2D and 3D detectors without necessitating retraining. It adopts a probabilistic-driven learning-based fusion framework, which is both computationally efficient—adding under 3ms latency on a desktop GPU—and effective, ranking at the top in the KITTI benchmark leaderboard among fusion-based methods.

Key Technical Details

1. Sparse Input Tensor Representation: CLOCs encodes 2D and 3D detection candidates into a sparse tensor, eliminating geometrically inconsistent data points. This representation efficiently handles the sparsity issue inherent in the data.
2. Fusion Network Architecture: The network architecture consists of sequential 2D convolution layers applied to the non-empty elements of the input tensor, culminating in a probability score map through max pooling.
3. Geometric and Semantic Consistencies: The fusion model incorporates the Intersection over Union (IoU) as a measure of geometric consistency, alongside semantic consistency to ensure aligned categories across modalities.
4. Robust Performance and Scalability: CLOCs demonstrates improved performance on the KITTI dataset, significantly enhancing the results achieved by both LiDAR-only and existing fusion-based approaches, especially at extended ranges. This system notably allows for scalable deployment across different detector configurations.

Experimental Results and Discussion

The CLOCs approach is quantitatively evaluated on the KITTI dataset, where it outperforms several state-of-the-art models in both 3D object and bird's-eye view detection metrics. The paper reports improvements in accuracy, particularly at longer distances from the sensor, addressing one of the pivotal challenges of relying solely on LiDAR systems.

The flexible integration with multiple detector variants, such as SECOND, PointRCNN, and PV-RCNN, enables the CLOCs system to adapt to different detection backbones, facilitating widespread applicability in research and industry scenarios.

Implications and Future Directions

The proposed CLOCs network illustrates how late fusion methods can effectively synthesize the strengths of LiDAR and camera data to produce superior detection results. By delivering computationally efficient performance enhancements and maintaining compatibility with existing detection frameworks, CLOCs could serve as a stepping stone toward more sophisticated multi-modal fusion systems in autonomous systems.

Future research might delve into enhancing the probabilistic models that underpin the fusion logic or extending this methodology to additional modalities and sensor types. Additionally, exploring adaptive or dynamic fusion mechanisms that account for real-time changes in environmental conditions presents further opportunities for advancing the field.

In conclusion, this paper presents significant contributions to multi-modal sensory fusion for 3D object detection, enriching the toolbox available to researchers and engineers working on perception systems for autonomous vehicles.