VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking (2303.11301v1)

Abstract: 3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra computation. In this paper, we instead propose VoxelNext for fully sparse 3D object detection. Our core insight is to predict objects directly based on sparse voxel features, without relying on hand-crafted proxies. Our strong sparse convolutional network VoxelNeXt detects and tracks 3D objects through voxel features entirely. It is an elegant and efficient framework, with no need for sparse-to-dense conversion or NMS post-processing. Our method achieves a better speed-accuracy trade-off than other mainframe detectors on the nuScenes dataset. For the first time, we show that a fully sparse voxel-based representation works decently for LIDAR 3D object detection and tracking. Extensive experiments on nuScenes, Waymo, and Argoverse2 benchmarks validate the effectiveness of our approach. Without bells and whistles, our model outperforms all existing LIDAR methods on the nuScenes tracking test benchmark.

• The paper introduces a voxel-to-object prediction scheme that bypasses dense proxy representations by directly utilizing sparse voxel features.
• It achieves a superior speed-accuracy trade-off with 60.0 mAP on nuScenes and significantly lowers computational cost to 33.6G FLOPs compared to traditional methods.
• The method eliminates post-processing steps like non-maximum suppression, streamlining the detection and tracking pipeline for real-time autonomous applications.

VoxelNeXt: Advancements in Sparse 3D Object Detection and Tracking

The paper "VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking" presents a novel approach for 3D object detection and tracking using sparse voxel features. Contrary to traditional methods that rely on hand-crafted proxies such as anchors or centers, this work introduces a fully sparse framework that predicts objects directly from sparse voxel features. This eliminates the need for dense prediction heads and non-maximum suppression (NMS) post-processing operations, resulting in a more efficient and streamlined process.

Key Contributions

The core contribution of VoxelNeXt is the introduction of a voxel-to-object prediction scheme, which effectively utilizes a strong sparse convolutional network for 3D object detection. This method avoids the proxy representations typically employed in previous approaches, allowing for a post-processing-free prediction strategy. VoxelNeXt significantly reduces computational overhead by sidestepping the dense convolutional heads and anchor-based methods prevalent in the field.

The paper reports that VoxelNeXt demonstrates a superior speed-accuracy trade-off compared to existing frameworks such as CenterPoint and PV-RCNN, particularly on the nuScenes dataset. This is a notable achievement as it showcases the potential of sparse voxel-based representation for effective LIDAR 3D object detection and tracking.

Numerical Results and Claims

The methodology of VoxelNeXt results in enhanced efficiency and performance across several benchmarks. For example, the paper states that on the nuScenes dataset, VoxelNeXt achieves a mAP of 60.0, outperforming CenterPoint's mAP of 58.6, while also providing a reduced computational footprint with only 33.6G FLOPs compared to CenterPoint's 123.7G FLOPs. Furthermore, VoxelNeXt ranks first among LIDAR-only entries on the nuScenes tracking test benchmark, highlighting its potential in real-time autonomous driving applications.

Practical and Theoretical Implications

Practically, the adoption of a fully sparse framework can lead to significant improvements in computational efficiency and operational latency, making it highly suitable for real-time applications in autonomous vehicles and robotics. The reduction in computational complexity without sacrificing accuracy is an appealing trait for deploying such systems in resource-constrained environments.

Theoretically, this work challenges the conventionally held belief that dense proxy frameworks are essential for accurate 3D object detection. By demonstrating that sparse voxel-based methods can achieve competitive results, the paper opens up new avenues for research in both model design and optimization techniques in the domain of sparse data representation.

Future Developments

The insights from this research suggest potential exploration of further refinement in sparse voxel networks, possibly integrating more advanced architectures that combine principles from both transformer networks and sparse convolutions. Future work may focus on broadening the application domains beyond autonomous driving, such as augmented reality or medical imaging, where sparse data is prevalent.

In conclusion, the VoxelNeXt framework represents a forward-thinking approach to 3D object detection and tracking, providing both a theoretically sound and practically efficient alternative to existing dense methods. Its ability to maintain high performance with reduced computational demands positions it as a key development in the evolution of sparse 3D perception technologies.