Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots (1912.12791v3)

Abstract: Accurate 3D object detection in LiDAR based point clouds suffers from the challenges of data sparsity and irregularities. Existing methods strive to organize the points regularly, e.g. voxelize, pass them through a designed 2D/3D neural network, and then define object-level anchors that predict offsets of 3D bounding boxes using collective evidences from all the points on the objects of interest. Contrary to the state-of-the-art anchor-based methods, based on the very nature of data sparsity, we observe that even points on an individual object part are informative about semantic information of the object. We thus argue in this paper for an approach opposite to existing methods using object-level anchors. Inspired by compositional models, which represent an object as parts and their spatial relations, we propose to represent an object as composition of its interior non-empty voxels, termed hotspots, and the spatial relations of hotspots. This gives rise to the representation of Object as Hotspots (OHS). Based on OHS, we further propose an anchor-free detection head with a novel ground truth assignment strategy that deals with inter-object point-sparsity imbalance to prevent the network from biasing towards objects with more points. Experimental results show that our proposed method works remarkably well on objects with a small number of points. Notably, our approach ranked 1st on KITTI 3D Detection Benchmark for cyclist and pedestrian detection, and achieved state-of-the-art performance on NuScenes 3D Detection Benchmark.

• The paper introduces "Object as Hotspots" (OHS), an anchor-free approach representing 3D objects via informative interior voxels to handle sparse LiDAR data.
• An innovative hotspot assignment strategy balances contributions from objects with different point densities, mitigating bias and improving detection robustness.
• The proposed method achieves state-of-the-art 3D detection performance on benchmarks like KITTI and NuScenes while maintaining real-time processing speeds.

Overview of "Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots"

The paper "Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots" addresses a core challenge in 3D object detection using LiDAR data: the intrinsic sparsity and irregularity of point clouds. Traditional state-of-the-art methods rely heavily on anchor-based techniques to predict 3D bounding boxes using collective evidence from point clouds of the objects in question. This paper proposes an innovative shift to an anchor-free approach that leverages individual voxels, termed "hotspots," within objects as informative units for detection.

Key Contributions

1. Object as Hotspots (OHS) Representation:
   • The paper introduces the concept of representing 3D objects through their non-empty interior voxels—hotspots—and the spatial relations among these hotspots. This approach contrasts with conventional anchor-based methods that often overlook the distinct parts of an object.
2. Anchor-Free Detection Head:
   • A unique detection scheme is proposed that forgoes anchor-based techniques. Instead, hotspots are dynamically selected based on the sparsity and volume of the object, enabling robust detection performance across varying densities of point clouds.
3. Hotspot Assignment Strategy:
   • An innovative ground truth assignment strategy balances the contribution of hotspots among objects with different point cloud densities, thereby mitigating model bias towards objects with more points.
4. Experimental Validation:
   • The proposed methodology is validated using benchmarks such as the KITTI 3D Detection and NuScenes 3D Detection datasets where it achieves state-of-the-art performance, particularly excelling in cyclist and pedestrian detection tasks.

Technical Insights

• Spatial Relation Encoding:
  • By incorporating spatial relation encoding, the model enhances discrimination between hotspots assigned different classes based on their relative spatial location within the object bounding box.
• Handling Regression Target Imbalance:
  • The paper implements the soft argmin technique from stereo vision to address the regression scale variance challenge, often observed in settings without predefined anchor sizes.
• Efficiency and Speed:
  • Despite the shift to an anchor-free paradigm, the method maintains real-time processing capability—25 FPS on the KITTI dataset—while providing substantial performance improvements over benchmark models.

Potential Implications

The concept of modeling objects as compositions of informative interior regions (hotspots) heralds a paradigm shift in handling 3D detection tasks. By focusing on the most discriminative features offered by the hotspots, the proposed OHS representation inherently supports configurations with fewer points across objects—this is particularly advantageous in conditions affected by occlusion or sparse sensory inputs.

Future Directions

The success of hotspots as a fundamental element for 3D object detection suggests several future research avenues, such as:

• Integration of Diverse Sensory Inputs:
  • The approach can be extended to process additional modalities like radar or image data, thereby enhancing detection robustness under challenging conditions.
• Efficient Training Strategies:
  • Investigating transfer learning or semi-supervised learning frameworks centered on hotspot features could further improve model adaptability across varying environmental setups.
• Exploration of Spatial Relation Encoding Techniques:
  • Further refinement in spatial relation encoding can provide more nuanced object identification abilities, particularly within complex environments featuring heavy occlusion.

In summary, this paper provides a methodologically sound pathway toward simplifying and enhancing 3D object detection by leveraging the unpredictably informative nature of sparse LiDAR data. Its anchor-free detection strategy paves the way for versatile, efficient, and increasingly adaptable autonomous systems.