Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion (2203.09780v2)

Abstract: Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different representations of images and point clouds make it difficult to fuse them, resulting in suboptimal performance. In this paper, we present a novel multi-modal framework SFD (Sparse Fuse Dense), which utilizes pseudo point clouds generated from depth completion to tackle the issues mentioned above. Different from prior works, we propose a new RoI fusion strategy 3D-GAF (3D Grid-wise Attentive Fusion) to make fuller use of information from different types of point clouds. Specifically, 3D-GAF fuses 3D RoI features from the couple of point clouds in a grid-wise attentive way, which is more fine-grained and more precise. In addition, we propose a SynAugment (Synchronized Augmentation) to enable our multi-modal framework to utilize all data augmentation approaches tailored to LiDAR-only methods. Lastly, we customize an effective and efficient feature extractor CPConv (Color Point Convolution) for pseudo point clouds. It can explore 2D image features and 3D geometric features of pseudo point clouds simultaneously. Our method holds the highest entry on the KITTI car 3D object detection leaderboard, demonstrating the effectiveness of our SFD. Codes are available at https://github.com/LittlePey/SFD.

• The paper introduces SFD, a novel multi-modal 3D detection framework that fuses sparse LiDAR and image data via depth completion.
• It employs grid-wise attentive fusion (3D-GAF) and synchronized augmentation (SynAugment) to enhance feature alignment and detection accuracy.
• Evaluations on the KITTI dataset show superior performance, especially in detecting occluded and distant objects in complex scenarios.

Insights into "Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion"

The paper "Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion" offers a fresh approach to multi-modal 3D detection by addressing the pervasive issue of point cloud sparsity inherent in LiDAR-only methods. The authors propose a framework, named SFD (Sparse Fuse Dense), which harmonizes data from LiDAR and imagery sources to achieve superior object detection outcomes.

The central innovation in this paper is the introduction of the multi-modal 3D detection framework that utilizes both depth completion and a novel Region of Interest (RoI) fusion strategy. Briefly, here's how SFD is structured:

1. Depth Completion to Generate Pseudo Point Clouds: By converting sparse LiDAR data and corresponding image data into dense pseudo point clouds, the paper proposes improving the granularity of 3D information available for detection. This enhanced data representation directly addresses limitations in object detection at long distances and through occlusions.
2. 3D Grid-wise Attentive Fusion (3D-GAF): The authors introduce a new RoI fusion method — 3D-GAF — which fuses features from the raw and pseudo point clouds in a grid-wise attentive manner. This approach is posited as more precise than prior methods and seeks to handle the alignment and individuality of object data across different modalities more effectively.
3. Synchronized Augmentation (SynAugment): SFD leverages data augmentation techniques commonly used in LiDAR-only methods by synchronizing the axis-aligned transformations between images and point clouds. This ensures the underlying model can exploit additional data variations during training, which were previously inaccessible to multi-modal methods due to the disparity in representation between 2D images and 3D LiDAR data.
4. Color Point Convolution (CPConv): For extracting features from the pseudo point clouds, the authors propose CPConv, which operates by exploring both the 3D geometry and the associated 2D image features stored in the coordinate space of the pseudo points. This feature extraction technique is optimized to maintain and utilize the richness of combined RGB image and depth-informed features.

The reported outcomes on the KITTI dataset reflect a significant enhancement in detection acuity, with SFD outstripping both mono-modal approaches like Voxel-RCNN as well as prior multi-modal methods. The results emphasized improvements especially in complex scenarios involving occluded or distant objects, showcasing a marked advantage in using synthetic point clouds for filling in the lack of raw point cloud density.

Implications and Future Directions

The practical implications of SFD's design are notable, especially in autonomous navigation systems where recognizing and tracking dynamic objects at long ranges can be crucial. The enhanced 3D detection performance facilitated by dense pseudo point clouds signifies a substantial step forward in developing safer, more accurate autonomous vehicles and remote sensing systems.

From a theoretical standpoint, the fusion strategy employed in 3D-GAF offers a structured method for integrating multi-view data representations, which could inspire further research into more complex fusion architectures. Additionally, the SynAugment strategy may prompt the development of even more powerful data augmentation techniques for multi-modal networks.

As the research field advances, one could speculate that future developments may seek to refine the efficiency of processing dense pseudo clouds, potentially through novel neural architectures that scale more gracefully with data volume. Moreover, integrating real-time depth completion within resource-constrained environments remains an open challenge, hinting at optimization opportunities.

Overall, the SFD framework's architecture and methodologies present a well-founded advancement in 3D detection, influencing future research and application scopes in computer vision and autonomous navigation domains.