GVSynergy-Det: Synergistic Gaussian-Voxel 3D Object Detection
Analysis
This paper addresses the challenge of 3D object detection from images without relying on depth sensors or dense 3D supervision. It introduces a novel framework, GVSynergy-Det, that combines Gaussian and voxel representations to capture complementary geometric information. The synergistic approach allows for more accurate object localization compared to methods that use only one representation or rely on time-consuming optimization. The results demonstrate state-of-the-art performance on challenging indoor benchmarks.
Key Takeaways
- •Proposes GVSynergy-Det, a novel framework for 3D object detection.
- •Combines Gaussian and voxel representations for synergistic feature extraction.
- •Achieves state-of-the-art results on ScanNetV2 and ARKitScenes datasets.
- •Does not require depth sensors or dense 3D supervision.
“Our key insight is that continuous Gaussian and discrete voxel representations capture complementary geometric information: Gaussians excel at modeling fine-grained surface details while voxels provide structured spatial context.”