Category-level object pose estimation is a longstanding and fundamental task crucial for augmented reality and robotic manipulation applications. Existing RGB-based approaches struggle with multi-stage settings and heavily rely on off-the-shelf techniques, such as object detectors, depth estimators, non-differentiable NOCS shape alignment, etc. Extra dependencies lead to the accumulation of errors and complicate the whole pipeline, limiting the deployment of these approaches in practical applications. This paper streamlined an end-to-end framework unifying the single-frame and video-based category-level pose estimation. Specifically, instead of explicitly introducing extra dependencies, the DINOv2 encoder and depth decoder, as robust semantic and geometric prior extractors, are leveraged to produce intra-frame hierarchical semantic and geometric features. A spatial-temporal sparse query network is developed to model the implicit correspondence and inter-frame correlations between a set of implicit 3D query anchors and intra-frame features. Finally, a pose prediction head is employed using the bipartite matching algorithm. Experimental results demonstrate that our model achieves state-of-the-art performance compared with RGB-based categorical pose estimation methods on the REAL275 and CAMERA25 datasets. Our code is available at https://andrewchiyz.github.io/vision.3dv.seqpose/.

Many robotics and industry applications have a high demand for the capability to estimate the 6D pose of novel objects from the cluttered scene. However, existing classic pose estimation methods are object-specific, which can only handle the specific objects seen during training. When applied to a novel object, these methods necessitate a cumbersome onboarding process, which involves extensive dataset preparation and model retraining. The extensive duration and resource consumption of onboarding limit their practicality in real-world applications In this paper, we introduce ZeroPose, a novel zero-shot framework that performs pose estimation following a Discovery-Orientation-Registration (DOR) inference pipeline. This framework generalizes to novel objects without requiring model retraining. Given the CAD model of a novel object, ZeroPose enables in seconds onboarding time to extract visual and geometric embeddings from the CAD model as a prompt. With the prompting of the above embeddings, DOR can discover all related instances and estimate their 6D poses without additional human interaction or presupposing scene conditions. Compared with existing zero-shot methods solved by the render-and-compare paradigm, the DOR pipeline formulates the object pose estimation into a feature-matching problem, which avoids time-consuming online rendering and improves efficiency. Experimental results on the seven datasets show that ZeroPose as a zero-shot method achieves comparable performance with object-specific training methods and outperforms the state-of-the-art zero-shot method with 50x inference speed improvement.

Direct pose estimation networks aim to directly regress the 6D poses of target objects in the scene image using a neural network. These direct methods offer efficiency and an optimal optimization target, presenting significant potential for practical applications. However, due to the complex and implicit mappings between input features and target pose parameters, direct methods are challenging to train and prone to overfitting on mappings seen during training, resulting in limited effectiveness and generalization capability on unseen mappings. Existing methods focus primarily on improvements of the network architecture and training strategies, with less attention given to mappings. In this work, we propose a geometric constraints learning approach, which enables networks to explicitly capture and utilize the geometric mappings between inputs and optimization targets for pose estimation. Specifically, we introduce a residual pose transformation formula that preserves pose transformation constraints within both the 2D image plane and the 3D space while decoupling the absolute pose distribution, thereby addressing the pose distribution gap issue. We further design a Geo6D mechanism based on the formula, which enables the network to explicitly utilize geometric constraints for pose estimation by reconstructing the inputs and outputs. We select two different methods as our baseline and extensive experiments show that Geo6D enhances the performance and reduces the dependence on extensive training data, remaining effective even with only 10% of the typical data volume.

Automatic multi-organ segmentation of the abdominal region is a critical yet challenging task in computer-aided medical image analysis. Recent advances in CNN- and Transformer-based encoder-decoder models tend to implicitly learn context features by using enhanced effective receptive fields to capture local and global range dependencies. However, due to the complex anatomical structure, those models cannot recover the anatomical topology properly and result in broken organs with inaccurate semantic labels. Therefore, in this paper, by considering the anatomy priors of multi-organs, we propose a Context Prior Network, namely CPNet, which integrates the 3D context semantic relations and geometry priors as explicit anatomical constraints. Specifically, a Semantic Relation Prior Propagation (SRPP) module is designed to propagate the semantic relations between voxels progressively. Moreover, a Multiple Context Prior Prediction (MCPP) module is adopted to preserve the accurate shape and topology by recovering 3D contours and surface normal. Experimental results demonstrate our proposed model outperforms state-of-the-art models for multi-organ segmentation on Abdomen CT and MRI datasets, especially for recovering organs with correct semantic labels and anatomical structures.

Uni6D is the first 6D pose estimation approach to employ a unified backbone network to extract features from both RGB and depth images. We discover that the principal reasons of Uni6D performance limitations are Instance-Outside and Instance-Inside noise. Uni6D’s simple pipeline design inherently introduces Instance-Outside noise from background pixels in the receptive field, while ignoring Instance-Inside noise in the input depth data. In this paper, we propose a two-step denoising approach for dealing with the aforementioned noise in Uni6D. To reduce noise from non-instance regions, an instance segmentation network is utilized in the first step to crop and mask the instance. A lightweight depth denoising module is proposed in the second step to calibrate the depth feature before feeding it into the pose regression network. Extensive experiments show that our Uni6Dv2 reliably and robustly eliminates noise, outperforming Uni6D without sacrificing too much inference efficiency. It also reduces the need for annotated real data that requires costly labeling.

Deep learning-based methods are continuously boosting the performance of detecting objects in natural images. On the contrary, detecting objects in Unmanned Aerial Vehicle (UAV) images remains to be a difficult task in the field of computer vision, due to the challenge of training a well-performed detection model working on UAV images which usually contain instances with varied orientations, scales, and contours, etc. Furthermore, only a few researchers have focused on this field, probably because of difficulties in UAV data acquisition and labelling. Inspired by this, we collected a large-scale dataset with multi-scale and high-resolution UAV images, named MOHR, which contains 10,631 images captured by a UAV affixed with three kinds of cameras. Since these images were captured in a suburban environment, we manually annotated five classes of objects, including car, truck, building, collapse and flood damage. An empirical study was then conducted by adopting six advanced object detection methods all of which are based on deep learning technologies. The results indicate the great potential of these evaluated object detection models, but also reveal that the research on such a challenging UAV dataset using current deep learning techniques is far reaching.