Weiyang Lin

Haoyuan Li, Ziqin Ye, Yue Hao et al.

Accurate object perception is essential for robotic applications such as object navigation. In this paper, we propose DQO-MAP, a novel object-SLAM system that seamlessly integrates object pose estimation and reconstruction. We employ 3D Gaussian Splatting for high-fidelity object reconstruction and leverage quadrics for precise object pose estimation. Both of them management is handled on the CPU, while optimization is performed on the GPU, significantly improving system efficiency. By associating objects with unique IDs, our system enables rapid object extraction from the scene. Extensive experimental results on object reconstruction and pose estimation demonstrate that DQO-MAP achieves outstanding performance in terms of precision, reconstruction quality, and computational efficiency. The code and dataset are available at: https://github.com/LiHaoy-ux/DQO-MAP.

Chaofei Qi, Chao Ye, Zhitai Liu et al.

Deep learning has witnessed the extensive utilization across a wide spectrum of domains, including fine-grained few-shot learning (FGFSL) which heavily depends on deep backbones. Nonetheless, shallower deep backbones such as ConvNet-4, are not commonly preferred because they're prone to extract a larger quantity of non-abstract visual attributes. In this paper, we initially re-evaluate the relationship between network depth and the ability to fully encode few-shot instances, and delve into whether shallow deep architecture could effectuate comparable or superior performance to mainstream deep backbone. Fueled by the inspiration from vanilla ConvNet-4, we introduce a location-aware constellation network (LCN-4), equipped with a cutting-edge location-aware feature clustering module. This module can proficiently encoder and integrate spatial feature fusion, feature clustering, and recessive feature location, thereby significantly minimizing the overall loss. Specifically, we innovatively put forward a general grid position encoding compensation to effectively address the issue of positional information missing during the feature extraction process of specific ordinary convolutions. Additionally, we further propose a general frequency domain location embedding technique to offset for the location loss in clustering features. We have carried out validation procedures on three representative fine-grained few-shot benchmarks. Relevant experiments have established that LCN-4 notably outperforms the ConvNet-4 based State-of-the-Arts and achieves performance that is on par with or superior to most ResNet12-based methods, confirming the correctness of our conjecture.