Estimating the geometric and volumetric properties of transparent deformable liquids is challenging due to optical complexities and dynamic surface deformations induced by container movements. Autonomous robots performing precise liquid manipulation tasks—such as dispensing, aspiration, and mixing—must handle containers in ways that inevitably induce these deformations, complicating accurate liquid state assessment. Current datasets lack comprehensive physics-informed simulation data representing realistic liquid behaviors under diverse dynamic scenarios. To bridge this gap, we introduce Phys-Liquid, a physics-informed dataset comprising 97,200 simulation images and corresponding 3D meshes, capturing liquid dynamics across multiple laboratory scenes, lighting conditions, liquid colors, and container rotations. To validate the realism and effectiveness of Phys-Liquid, we propose a four-stage reconstruction and estimation pipeline involving liquid segmentation, multi-view mask generation, 3D mesh reconstruction, and real-world scaling. Experimental results demonstrate improved accuracy and consistency in reconstructing liquid geometry and volume, outperforming existing benchmarks. The dataset and associated validation methods facilitate future advancements in transparent liquid perception tasks.

Recent advances in machine learning have greatly benefited object detection and 6D pose estimation. However, textureless and metallic objects still pose a significant challenge due to few visual cues and the texture bias of CNNs. To address this issue, we propose a strategy for inducing a shape bias to CNN training. In particular, by randomizing textures applied to object surfaces during data rendering, we create training data without consistent textural cues. This methodology allows for seamless integration into existing data rendering engines, and results in negligible computational overhead for data rendering and network training. Our findings demonstrate that the shape bias we induce via randomized texturing, improves over existing approaches using style transfer. We evaluate with five detectors and two pose estimators. For three object detectors and for pose estimation in general, estimation accuracy improves for textureless and metallic objects. Additionally we show that our approach increases the pose estimation accuracy in the presence of image noise and strong illumination changes. Code available at https://github.com/hoenigpeter/randomized_texturing.

As robotic systems increasingly encounter complex and unconstrained real-world scenarios, there is a demand to recognize diverse objects. The state-of-the-art 6D object pose estimation methods rely on object-specific training and therefore do not generalize to unseen objects. Recent novel object pose estimation methods are solving this issue using task-specific fine-tuned CNNs for deep template matching. This adaptation for pose estimation still requires expensive data rendering and training procedures. MegaPose for example is trained on a dataset consisting of two million images showing 20,000 different objects to reach such generalization capabilities. To overcome this shortcoming we introduce ZS6D, for zero-shot novel object 6D pose estimation. Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are used for matching rendered templates against query images of objects and for establishing local correspondences. These local correspondences enable deriving geometric correspondences and are used for estimating the object's 6D pose with RANSAC- based PnP. This approach showcases that the image descriptors extracted by pre-trained ViTs are well-suited to achieve a notable improvement over two state-of-the-art novel object 6D pose estimation methods, without the need for task-specific fine-tuning. Experiments are performed on LMO, YCBV, and TLESS. In comparison to MegaPose, we improve the Average Recall on all three datasets and compared to OSOP we improve on two datasets. The code is available at https://github.com/PhilippAuss/ZS6D.

Object pose estimation is a core perception task that enables, for example, object manipulation and scene understanding. The widely available, inexpensive, and high-resolution RGB sensors and CNNs that allow for fast inference make monocular approaches especially well-suited for robotics applications. We observe that previous surveys establish the state of the art for varying modalities, single- and multiview settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central and largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved.

Robots operating in the real world are expected to detect, classify, segment, and estimate the pose of objects to accomplish their task. Modern approaches using deep learning not only require large volumes of data but also pixel-accurate annotations in order to evaluate the performance and therefore safety of these algorithms. At present, publicly available tools for annotating data are scarce and those that are available rely on depth sensors, which excludes their use for transparent, metallic, and general non-Lambertian objects. To address this issue, we present a novel method for creating valuable datasets that can be used in these more difficult cases. Our key contribution is a purely RGB-based scene-level annotation approach that uses a neural radiance field-based method to automatically align objects. A set of user studies demonstrates the accuracy and speed of our approach over a purely manual or depth sensor assisted pipeline. We provide an open-source implementation of each component and a ROS-based recorder for capturing data with a eye-in-hand robot system. Code will be made available at https://github.com/markus-suchi/3D-DAT.

Object pose estimation is important for object manipulation and scene understanding. In order to improve the general applicability of pose estimators, recent research focuses on providing estimates for novel objects, that is, objects unseen during training. Such works use deep template matching strategies to retrieve the closest template connected to a query image, which implicitly provides object class and pose. Despite the recent success and improvements of Vision Transformers over CNNs for many vision tasks, the state of the art uses CNN-based approaches for novel object pose estimation. This work evaluates and demonstrates the differences between self-supervised CNNs and Vision Transformers for deep template matching. In detail, both types of approaches are trained using contrastive learning to match training images against rendered templates of isolated objects. At test time such templates are matched against query images of known and novel objects under challenging settings, such as clutter, occlusion and object symmetries, using masked cosine similarity. The presented results not only demonstrate that Vision Transformers improve matching accuracy over CNNs but also that for some cases pre-trained Vision Transformers do not need fine-tuning to achieve the improvement. Furthermore, we highlight the differences in optimization and network architecture when comparing these two types of networks for deep template matching.

Object classification is an important capability for robots as it provides vital semantic information that underpin most practical high-level tasks. Classic handcrafted features, such as point pair features, have demonstrated their robustness for this task. Combining these features with modern deep learning methods provide discriminative features that are rotation invariant and robust to various sources of noise. In this work, we aim to improve the descriptiveness of point pair features while retaining their robustness. We propose a method to achieve more structured sampling of pairs and combine this information through the use of graph convolutional networks. We introduce a novel attention model based on a repeatable local reference frame. Experiments show that our approach significantly improves the state of the art for object classification on large scale reconstruction such as the Stanford 3D indoor dataset and ScanNet and obtains competitive accuracy on the artificial dataset ModelNet.

Visual attention modelling is an important research topic with a wide range of applications in visual tracking, perceptual quality assessment, re-targeting, video summarization, etc. In this paper, we propose a visual attention model that captures both bottom-up spatial-temporal saliency and top-down objectness. Leveraging on co-occurrence histograms, the proposed model captures a number of low-level cues including contrast, gradient, as well as, magnitude and gradient of optical flow. Additionally, the proposed model incorporates mid-level objectness cue which helps to boost the modelling performance greatly. The proposed model obtained superior AUC-ROCs when evaluated over the ASCMN dataset and the UCF Sports Action dataset.

RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios. While many research has been conducted, most work used hand-crafted features which are difficult to capture high-level semantic structures. Recently, the feature extracted from deep convolutional neural network has produced state-of-the-art results for various computer vision tasks, which inspire researchers to explore incorporating CNN learned features for RGBD scene understanding. On the other hand, most existing work combines rgb and depth features without adequately exploiting the consistency and complementary information between them. Inspired by some recent work on RGBD object recognition using multi-modal feature fusion, we introduce a novel discriminative multi-modal fusion framework for rgbd scene recognition for the first time which simultaneously considers the inter-and intra-modality correlation for all samples and meanwhile regularizing the learned features to be discriminative and compact. The results from the multimodal layer can be back-propagated to the lower CNN layers, hence the parameters of the CNN layers and multimodal layers are updated iteratively until convergence. Experiments on the recently proposed large scale SUN RGB-D datasets show that our method achieved the state-of-the-art without any image segmentation.