Distributed multi-stage image compression—where visual content traverses multiple processing nodes under varying quality requirements—poses challenges. Progressive methods enable bitstream truncation but underutilize available compute resources; successive compression repeats costly pixel-domain operations and suffers cumulative quality loss and inefficiency; fixed-parameter models lack post-encoding flexibility. In this work, we developed the Hierarchical Cascade Framework (HCF) that achieves high rate-distortion performance and better computational efficiency through direct latent-space transformations across network nodes in distributed multi-stage image compression systems. Under HCF, we introduced policy-driven quantization control to optimize rate–distortion trade-offs, and established the edge quantization principle through differential entropy analysis. The configuration based on this principle demonstrates up to 0.6dB PSNR gains over other configurations. When comprehensively evaluated on the Kodak, CLIC, and CLIC2020-mobile datasets, HCF outperforms successive-compression methods by up to 5.56% BD-Rate in PSNR on CLIC, while saving up to 97.8% FLOPs, 96.5% GPU memory, and 90.0% execution time. It also outperforms state-of-the-art progressive compression methods by up to 12.64% BD-Rate on Kodak and enables retraining-free cross-quality adaptation with 7.13-10.87% BD-Rate reductions on CLIC2020-mobile.

Generalizable 3D object reconstructionfrom single-view RGB-D images remains a challenging task, particularly with real-world data. Current state-of-the-art methods develop Transformer-based implicit field learning, necessitating an intensive learning paradigm that requires dense query-supervision uniformly sampled throughout the entire space. We propose a novel approach, IPoD, which harmonizes implicit field learning with point diffusion. This approach treats the query points for implicit field learning as a noisy point cloud for iterative denoising, allowing for their dynamic adaptation to the target object shape. Such adaptive query points harness diffusion learning's capability for coarse shape recovery and also enhances the implicit representation's ability to delineate finer details. Besides, an additional self-conditioning mechanism is designed to use implicit predictions as the guidance of diffusion learning, leading to a cooperative system. Experiments conducted on the CO3D-v2 dataset affirm the superiority of IPoD, achieving 7.8% improvement in F-score and 28.6% in Chamfer distance over existing methods. The generalizability of IPoD is also demonstrated on the MVImgNet dataset. Our project page is at https://yushuang-wu.github.io/IPoD.

This paper studies the problem of estimating physical properties (system identification) through visual observations. To facilitate geometry-aware guidance in physical property estimation, we introduce a novel hybrid framework that leverages 3D Gaussian representation to not only capture explicit shapes but also enable the simulated continuum to render object masks as 2D shape surrogates during training. We propose a new dynamic 3D Gaussian framework based on motion factorization to recover the object as 3D Gaussian point sets across different time states. Furthermore, we develop a coarse-to-fine filling strategy to generate the density fields of the object from the Gaussian reconstruction, allowing for the extraction of object continuums along with their surfaces and the integration of Gaussian attributes into these continuum. In addition to the extracted object surfaces, the Gaussian-informed continuum also enables the rendering of object masks during simulations, serving as 2D-shape guidance for physical property estimation. Extensive experimental evaluations demonstrate that our pipeline achieves state-of-the-art performance across multiple benchmarks and metrics. Additionally, we illustrate the effectiveness of the proposed method through real-world demonstrations, showcasing its practical utility. Our project page is at https://jukgei.github.io/project/gic.

This paper tackles the task of singulating and grasping paper-like deformable objects. We refer to such tasks as paper-flipping. In contrast to manipulating deformable objects that lack compression strength (such as shirts and ropes), minor variations in the physical properties of the paper-like deformable objects significantly impact the results, making manipulation highly challenging. Here, we present Flipbot, a novel solution for flipping paper-like deformable objects. Flipbot allows the robot to capture object physical properties by integrating exteroceptive and proprioceptive perceptions that are indispensable for manipulating deformable objects. Furthermore, by incorporating a proposed coarse-to-fine exploration process, the system is capable of learning the optimal control parameters for effective paper-flipping through proprioceptive and exteroceptive inputs. We deploy our method on a real-world robot with a soft gripper and learn in a self-supervised manner. The resulting policy demonstrates the effectiveness of Flipbot on paper-flipping tasks with various settings beyond the reach of prior studies, including but not limited to flipping pages throughout a book and emptying paper sheets in a box. The code is available here: https://robotll.github.io/Flipbot/.

Grasping a wide range of novel objects in densely cluttered scenes is difficult due to irregular shapes of objects and the uncertainty in sensing. In this paper, a novel vacuum cup grasping method, based on uncertainty modeling of perception data and grasp geometric heuristics, is proposed to grasp unknown objects in densely cluttered scenes. The probabilistic signed distance function is proposed to both reconstruct the point cloud of a scene and explicitly model the uncertainty from depth images captured from a low-cost stereo camera. The quasi-static spring model is used to approximate seal formation between the suction cup and the reconstructed point cloud. A coarse-to-fine exploration procedure is proposed to refine the estimated point cloud, reduce uncertainties during the movement of the robot and redetermine the target grasp pose iteratively. Extensive experiments show that our proposed method achieves state-of-the-art performance on real-world grasping and outperforms existing methods by a large margin.

3D object detection has been wildly studied in recent years, especially for robot perception systems. However, existing 3D object detection is under a closed-set condition, meaning that the network can only output boxes of trained classes. Unfortunately, this closed-set condition is not robust enough for practical use, as it will identify unknown objects as known by mistake. Therefore, in this paper, we propose an open-set 3D object detector, which aims to (1) identify known objects, like the closed-set detection, and (2) identify unknown objects and give their accurate bounding boxes. Specifically, we divide the open-set 3D object detection problem into two steps: (1) finding out the regions containing the unknown objects with high probability and (2) enclosing the points of these regions with proper bounding boxes. The first step is solved by the finding that unknown objects are often classified as known objects with low confidence, and we show that the Euclidean distance sum based on metric learning is a better confidence score than the naive softmax probability to differentiate unknown objects from known objects. On this basis, unsupervised clustering is used to refine the bounding boxes of unknown objects. The proposed method combining metric learning and unsupervised clustering is called the MLUC network. Our experiments show that our MLUC network achieves state-of-the-art performance and can identify both known and unknown objects as expected.

Classical close-set semantic segmentation networks have limited ability to detect out-of-distribution (OOD) objects, which is important for safety-critical applications such as autonomous driving. Incrementally learning these OOD objects with few annotations is an ideal way to enlarge the knowledge base of the deep learning models. In this paper, we propose an open world semantic segmentation system that includes two modules: (1) an open-set semantic segmentation module to detect both in-distribution and OOD objects. (2) an incremental few-shot learning module to gradually incorporate those OOD objects into its existing knowledge base. This open world semantic segmentation system behaves like a human being, which is able to identify OOD objects and gradually learn them with corresponding supervision. We adopt the Deep Metric Learning Network (DMLNet) with contrastive clustering to implement open-set semantic segmentation. Compared to other open-set semantic segmentation methods, our DMLNet achieves state-of-the-art performance on three challenging open-set semantic segmentation datasets without using additional data or generative models. On this basis, two incremental few-shot learning methods are further proposed to progressively improve the DMLNet with the annotations of OOD objects.

In 6D object pose estimation task, object models are usually available and represented as the point cloud set in canonical object frame, which are important references for estimating object poses to the camera frame. However, directly introducing object models as the prior knowledge (i.e., object model point cloud) will cause potential perturbations and even degenerate pose estimation performance. To make the most of object model priors and eliminate the problem, we present an end-to-end deep learning approach called the Geometric Constraint Co-attention Network (GCCN) for 6D object pose estimation. GCCN is designed to explicitly leverage the object model priors effectively with the co-attention mechanism. We add explicit geometric constraints to a co-attention module to inform the geometric correspondence relationships between points in the scene and object model priors and develop a novel geometric constraint loss to guide the training. In this manner, our method effectively eliminates the side effect of directly introducing the object model priors into the network. Experiments on the YCB-Video and LineMOD datasets demonstrate that our GCCN substantially improves the performance of pose estimation and is robust against heavy occlusions. We also demonstrate that GCCN is accurate and robust enough to be deployed in real-world robotic tasks.

Instance grasping is a challenging robotic grasping task when a robot aims to grasp a specified target object in cluttered scenes. In this paper, we propose a novel end-to-end instance grasping method using only monocular workspace and query images, where the workspace image includes several objects and the query image only contains the target object. To effectively extract discriminative features and facilitate the training process, a learning-based method, referred to as Constraint Co-Attention Network (CCAN), is proposed which consists of a constraint co-attention module and a grasp affordance predictor. An effective co-attention module is presented to construct the features of a workspace image from the extracted features of the query image. By introducing soft constraints into the co-attention module, it highlights the target object's features while trivializes other objects' features in the workspace image. Using the features extracted from the co-attention module, the cascaded grasp affordance interpreter network only predicts the grasp configuration for the target object. The training of the CCAN is totally based on simulated self-supervision. Extensive qualitative and quantitative experiments show the effectiveness of our method both in simulated and real-world environments even for totally unseen objects.

Data-driven approach for grasping shows significant advance recently. But these approaches usually require much training data. To increase the efficiency of grasping data collection, this paper presents a novel grasp training system including the whole pipeline from data collection to model inference. The system can collect effective grasp sample with a corrective strategy assisted by antipodal grasp rule, and we design an affordance interpreter network to predict pixelwise grasp affordance map. We define graspability, ungraspability and background as grasp affordances. The key advantage of our system is that the pixel-level affordance interpreter network trained with only a small number of grasp samples under antipodal rule can achieve significant performance on totally unseen objects and backgrounds. The training sample is only collected in simulation. Extensive qualitative and quantitative experiments demonstrate the accuracy and robustness of our proposed approach. In the real-world grasp experiments, we achieve a grasp success rate of 93% on a set of household items and 91% on a set of adversarial items with only about 6,300 simulated samples. We also achieve 87% accuracy in clutter scenario. Although the model is trained using only RGB image, when changing the background textures, it also performs well and can achieve even 94% accuracy on the set of adversarial objects, which outperforms current state-of-the-art methods.

A cognitive robot usually needs to perform multiple tasks in practice and needs to locate the desired area for each task. Since deep learning has achieved substantial progress in image recognition, to solve this area detection problem, it is straightforward to label a functional area (affordance) image dataset and apply a well-trained deep-model-based classifier on all the potential image regions. However, annotating the functional area is time consuming and the requirement of large amount of training data limits the application scope. We observe that the functional area are usually related to the surrounding object context. In this work, we propose to use the existing object detection dataset and employ the object context as effective prior to improve the performance without additional annotated data. In particular, we formulate a two-stream network that fuses the object-related and functionality-related feature for functional area detection. The whole system is formulated in an end-to-end manner and easy to implement with current object detection framework. Experiments demonstrate that the proposed network outperforms current method by almost 20% in terms of precision and recall.