Distilling 3D distinctive local descriptors for 6D pose estimation

Three-dimensional local descriptors are crucial for encoding geometric surface properties, making them essential for various point cloud understanding tasks. Among these descriptors, GeDi has demonstrated strong zero-shot 6D pose estimation capabilities but remains computationally impractical for real-world applications due to its expensive inference process. Can we retain GeDi's effectiveness while significantly improving its efficiency? In this paper, we explore this question by introducing a knowledge distillation framework that trains an efficient student model to regress local descriptors from a GeDi teacher. Our key contributions include: an efficient large-scale training procedure that ensures robustness to occlusions and partial observations while operating under compute and storage constraints, and a novel loss formulation that handles weak supervision from non-distinctive teacher descriptors. We validate our approach on five BOP Benchmark datasets and demonstrate a significant reduction in inference time while maintaining competitive performance with existing methods, bringing zero-shot 6D pose estimation closer to real-time feasibility. Project Website: https://tev-fbk.github.io/dGeDi/

Object 6D pose estimation meets zero-shot learning

Object 6D pose estimation methods can achieve high accuracy when trained and tested on the same objects. However, estimating the pose of objects that are absent at training time is still a challenge. In this work, we advance the state-of-the-art in zero-shot object 6D pose estimation by proposing the first method that fuses the contribution of pre-trained geometric and vision foundation models. Unlike state-of-the-art approaches that train their pipeline on data specifically crafted for the 6D pose estimation task, our method does not require task-specific finetuning. Instead, our method, which we name PoMZ, combines geometric descriptors learned from point cloud data with visual features learned from large-scale web images to produce distinctive 3D point-level descriptors. By applying an off-the-shelf registration algorithm, like RANSAC, PoMZ outperforms all state-of-the-art zero-shot object 6D pose estimation approaches. We extensively evaluate PoMZ across the seven core datasets of the BOP Benchmark, encompassing over a hundred objects and 20 thousand images captured in diverse scenarios. PoMZ ranks first in the BOP Benchmark under the category Task 4: 6D localization of unseen objects. We will release the source code publicly.

Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image Segmentation

Timely identification and treatment of rapidly progressing skin cancers can significantly contribute to the preservation of patients' health and well-being. Dermoscopy, a dependable and accessible tool, plays a pivotal role in the initial stages of skin cancer detection. Consequently, the effective processing of digital dermoscopy images holds significant importance in elevating the accuracy of skin cancer diagnoses. Multilevel thresholding is a key tool in medical imaging that extracts objects within the image to facilitate its analysis. In this paper, an enhanced version of the Mud Ring Algorithm hybridized with the Whale Optimization Algorithm, named WMRA, is proposed. The proposed approach utilizes bubble-net attack and mud ring strategy to overcome stagnation in local optima and obtain optimal thresholds. The experimental results show that WMRA is powerful against a cluster of recent methods in terms of fitness, Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE).