The 3D Poses in the Wild dataset is the first dataset in the wild with accurate 3D poses for evaluation. While other datasets outdoors exist, they are all restricted to a small recording volume. 3DPW is the first one that includes video footage taken from a moving phone camera.
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Objaverse is a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category.
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ShapeNetCore is a subset of the full ShapeNet dataset with single clean 3D models and manually verified category and alignment annotations. It covers 55 common object categories with about 51,300 unique 3D models. The 12 object categories of PASCAL 3D+, a popular computer vision 3D benchmark dataset, are all covered by ShapeNetCore.
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HumanML3D is a 3D human motion-language dataset that originates from a combination of HumanAct12 and Amass dataset. It covers a broad range of human actions such as daily activities (e.g., 'walking', 'jumping'), sports (e.g., 'swimming', 'playing golf'), acrobatics (e.g., 'cartwheel') and artistry (e.g., 'dancing'). Overall, HumanML3D dataset consists of 14,616 motions and 44,970 descriptions composed by 5,371 distinct words. The total length of motions amounts to 28.59 hours. The average motion length is 7.1 seconds, while average description length is 12 words.
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ReferIt3D provides two large-scale and complementary visio-linguistic datasets: i) Sr3D, which contains 83.5K template-based utterances leveraging spatial relations among fine-grained object classes to localize a referred object in a scene, and ii) Nr3D which contains 41.5K natural, free-form, utterances collected by deploying a 2-player object reference game in 3D scenes. This dataset can be used for 3D visual grounding and 3D dense captioning tasks.
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BEAT has i) 76 hours, high-quality, multi-modal data captured from 30 speakers talking with eight different emotions and in four different languages, ii) 32 millions frame-level emotion and semantic relevance annotations. Our statistical analysis on BEAT demonstrates the correlation of conversational gestures with \textit{facial expressions}, \textit{emotions}, and \textit{semantics}, in addition to the known correlation with \textit{audio}, \textit{text}, and \textit{speaker identity}. Based on this observation, we propose a baseline model, \textbf{Ca}scaded \textbf{M}otion \textbf{N}etwork \textbf{(CaMN)}, which consists of above six modalities modeled in a cascaded architecture for gesture synthesis. To evaluate the semantic relevancy, we introduce a metric, Semantic Relevance Gesture Recall (\textbf{SRGR}). Qualitative and quantitative experiments demonstrate metrics' validness, ground truth data quality, and baseline's state-of-the-art performance. To the best of our knowledge,
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Gait3D is a large-scale 3D representation-based gait recognition dataset. It contains 4,000 subjects and over 25,000 sequences extracted from 39 cameras in an unconstrained indoor scene.
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ModelNet40-C is a comprehensive dataset to benchmark the corruption robustness of 3D point cloud recognition.
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A large-scale V2X perception dataset using CARLA and OpenCDA
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The REALY benchmark aims to introduce a region-aware evaluation pipeline to measure the fine-grained normalized mean square error (NMSE) of 3D face reconstruction methods from under-controlled image sets.
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PointCloud-C is the very first test-suite for point cloud robustness analysis under corruptions.
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Toyota Smarthome Trimmed has been designed for the activity classification task of 31 activities. The videos were clipped per activity, resulting in a total of 16,115 short RGB+D video samples. activities were performed in a natural manner. As a result, the dataset poses a unique combination of challenges: high intra-class variation, high-class imbalance, and activities with similar motion and high duration variance. Activities were annotated with both coarse and fine-grained labels. These characteristics differentiate Toyota Smarthome Trimmed from other datasets for activity classification.
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HPS Dataset is a collection of 3D humans interacting with large 3D scenes (300-1000 $m^2$, up to 2500 $m^2$). The dataset contains images captured from a head-mounted camera coupled with the reference 3D pose and location of the person in a pre-scanned 3D scene. 7 people in 8 large scenes are captured performing activities such as exercising, reading, eating, lecturing, using a computer, making coffee, dancing. The dataset provides more than 300K synchronized RGB images coupled with the reference 3D pose and location.
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SQA3D is a dataset for embodied scene understanding, where an agent needs to comprehend the scene it situates from an first person's perspective and answer questions. The questions are designed to be situated, embodied and knowledge-intensive. We offer three different modalities to represent a 3D scene: 3D scan, egocentric video and BEV picture.
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CustomHumans is recorded by a multi-view photogrammetry system equipped with 53 RGB (12 Megapixels) and 53 (4 Megapixels) IR cameras. The resulting high-quality scan is composed of a 40K-face mesh alongside a 4K-resolution texture map. In addition to the high-quality scans, CustomHumans provides accurately registered SMPL-X parameters using a customized mesh registration pipeline. 80 participants are invited for the data capturing. Each of them is instructed to perform several movements, such as "T-pose", "Hands Up'", "Squat'", "Turing head'', and "Hand gestures", in a 10-second long sequence (300 frames). 4-5 best-quality meshes in each sequence are selected as the data samples. In total, the dataset contains more than 600 high-quality scans with 120 different garments.
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4D-OR includes a total of 6734 scenes, recorded by six calibrated RGB-D Kinect sensors 1 mounted to the ceiling of the OR, with one frame-per-second, providing synchronized RGB and depth images. We provide fused point cloud sequences of entire scenes, automatically annotated human 6D poses and 3D bounding boxes for OR objects. Furthermore, we provide SSG annotations for each step of the surgery together with the clinical roles of all the humans in the scenes, e.g., nurse, head surgeon, anesthesiologist.
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We provide manual annotations of 14 semantic keypoints for 100,000 car instances (sedan, suv, bus, and truck) from 53,000 images captured from 18 moving cameras at Multiple intersections in Pittsburgh, PA. Please fill the google form to get a email with the download links:
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Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Housekeep a benchmark to evaluate common sense reasoning in the home for embodied AI. In Housekeep, an embodied agent must tidy a house by rearranging misplaced objects without explicit instructions specifying which objects need to be rearranged. The dataset contains where humans typically place objects in tidy and untidy houses constituting 1799 objects, 268 object categories, 585 placements, and 105 rooms.
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The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy.
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Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=1271), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes training (public. n=655), test (masks hidden, n=300), and generalizability (completely hidden, n=316) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test and generalizability datasets allow for unbiased performance evaluation
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CMD is a publicly available collection of hundreds of thousands 2D maps and 3D grids containing different properties of the gas, dark matter, and stars from more than 2,000 different universes. The data has been generated from thousands of state-of-the-art (magneto-)hydrodynamic and gravity-only N-body simulations from the CAMELS project.
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Tasks. In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. Therefore, the input to all evaluated methods is a list of coordinates of the three-dimensional points along with their remission, i.e., the strength of the reflected laser beam which depends on the properties of the surface that was hit. Each method should then output a label for each point of a scan, i.e., one full turn of the rotating LiDAR sensor. Here, we only distinguish between static and moving object classes.
The MMBody dataset provides human body data with motion capture, GT mesh, Kinect RGBD, and millimeter wave sensor data. See homepage for more details.
Generate high-quality 3D ground-truth shapes for reconstruction evaluation is extremely challenging because even 3D scanners can only generate pseudo ground-truth shapes with artefacts. We propose a novel data capturing and 3D annotation pipeline to obtain precise 3D ground-truth shapes without relying on expensive 3D scanners. The key to creating the precise 3D ground-truth shapes is using LEGO models, which are made of LEGO bricks with known geometry. The MobileBrick dataset provides a unique opportunity for future research on high-quality 3D reconstruction thanks to two distinctive features: 1) A large number of RGBD sequences with precise 3D ground-truth annotations. 2) The RGBD images were captured using mobile devices so algorithms can be tested in a realistic setup for mobile AR applications.
Understanding spatial relations (e.g., “laptop on table”) in visual input is important for both humans and robots. Existing datasets are insufficient as they lack largescale, high-quality 3D ground truth information, which is critical for learning spatial relations. In this paper, we fill this gap by constructing Rel3D: the first large-scale, human-annotated dataset for grounding spatial relations in 3D. Rel3D enables quantifying the effectiveness of 3D information in predicting spatial relations on large-scale human data. Moreover, we propose minimally contrastive data collection—a novel crowdsourcing method for reducing dataset bias. The 3D scenes in our dataset come in minimally contrastive pairs: two scenes in a pair are almost identical, but a spatial relation holds in one and fails in the other. We empirically validate that minimally contrastive examples can diagnose issues with current relation detection models as well as lead to sample-efficient training. Code and data are avai
T$^3$Bench is the first comprehensive text-to-3D benchmark containing diverse text prompts of three increasing complexity levels that are specially designed for 3D generation (300 prompts in total). To assess both the subjective quality and the text alignment, we propose two automatic metrics based on multi-view images produced by the 3D contents. The quality metric combines multi-view text-image scores and regional convolution to detect quality and view inconsistency. The alignment metric uses multi-view captioning and Large Language Model (LLM) evaluation to measure text-3D consistency.
DurLAR is a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery for multi-modal autonomous driving applications. Compared to existing autonomous driving task datasets, DurLAR has the following novel features:
5 PAPERS • NO BENCHMARKS YET
Cancer in the region of the head and neck (HaN) is one of the most prominent cancers, for which radiotherapy represents an important treatment modality that aims to deliver a high radiation dose to the targeted cancerous cells while sparing the nearby healthy organs-at-risk (OARs). A precise three-dimensional spatial description, i.e. segmentation, of the target volumes as well as OARs is required for optimal radiation dose distribution calculation, which is primarily performed using computed tomography (CT) images. However, the HaN region contains many OARs that are poorly visible in CT, but better visible in magnetic resonance (MR) images. Although attempts have been made towards the segmentation of OARs from MR images, so far there has been no evaluation of the impact the combined analysis of CT and MR images has on the segmentation of OARs in the HaN region. The Head and Neck Organ-at-Risk Multi-Modal Segmentation Challenge aims to promote the development of new and application of
The RAD-ChestCT dataset is a large medical imaging dataset developed by Duke MD/PhD Rachel Draelos during her Computer Science PhD supervised by Lawrence Carin. The full dataset includes 35,747 chest CT scans from 19,661 adult patients. The public Zenodo repository contains an initial release of 3,630 chest CT scans, approximately 10% of the dataset. This dataset is of significant interest to the machine learning and medical imaging research communities.
The Zenseact Open Dataset (ZOD) is a large-scale and diverse multi-modal autonomous driving (AD) dataset, created by researchers at Zenseact. It was collected over a 2-year period in 14 different European counties, using a fleet of vehicles equipped with a full sensor suite. The dataset consists of three subsets: Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatiotemporal learning, sensor fusion, localization, and mapping.
Ubisoft La Forge Animation Dataset ("LAFAN1") Ubisoft La Forge Animation dataset and accompanying code for the SIGGRAPH 2020 paper Robust Motion In-betweening.
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Endoscopic stereo reconstruction for surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surface properties, and the presence of blood and smoke. These issues present difficulties for both stereo reconstruction itself and also for standardised dataset production. We present a stereo-endoscopic reconstruction validation dataset based on cone-beam CT (SERV-CT). Two ex vivo small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Subsequent orientation of the endoscope was manually aligned to match the stereoscopic view and benchmark disparities, depths and occlusions are calculated. The requirement of a CT scan limited the number of stereo pairs to 8 from each ex vivo sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of around
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Purpose Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered cone-beam CT (CBCT) and magnetic resonance imaging (MRI) images to facilitate the development and evaluation of sCT generation for radiotherapy planning.
This is a 16.2-million frame (50-hour) multimodal dataset of two-person face-to-face spontaneous conversations. This dataset features synchronized body and finger motion as well as audio data. It represents the largest motion capture and audio dataset of natural conversations to date. The statistical analysis verifies strong intraperson and interperson covariance of arm, hand, and speech features, potentially enabling new directions on data-driven social behavior analysis, prediction, and synthesis.
A new dataset with significant occlusions related to object manipulation.
We established a 3D evaluation benchmark, 3D MM-Vet, severing as assessing the 4-level capacity in embodied interaction scenarios, varying from basic perception to control statements generation.
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How and where proteins interface with one another can ultimately impact the proteins' functions along with a range of other biological processes. As such, precise computational methods for protein interface prediction (PIP) come highly sought after as they could yield significant advances in drug discovery and design as well as protein function analysis. However, the traditional benchmark dataset for this task, Docking Benchmark 5 (DB5), contains only a paltry 230 complexes for training, validating, and testing different machine learning algorithms. In this work, we expand on a dataset recently introduced for this task, the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for geometric deep learning of protein interfaces. The previous version of DIPS contains only the Cartesian coordinates and types of the atoms comprising a given protein complex, whereas DIPS-Plus now includes a plethora of new residue-level
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The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes.
The Household Object Movements from Everyday Routines (HOMER) dataset is composed of routine behaviors for five households, spanning 50 days for the train split and 10 days for test split. The households are based on an identical apartment setting with four rooms and 108 objects and 33 atomic actions such as find, grab, etc.
The NVIDIA HOPE datasets consist of RGBD images and video sequences with labeled 6-DoF poses for 28 toy grocery objects. The toy grocery objects are readily available for purchase and have ideal size and weight for robotic manipulation. 3D textured meshes for generating synthetic training data are provided.
OpenTrench3D, the first publicly available point cloud dataset of underground utilities from open trenches. It features 310 fully annotated point clouds consisting of a total of 528 million points categorised into 5 unique classes. OpenTrench3D consists of photogrammetrically derived 3D point clouds capturing detailed scenes of open trenches, revealing underground utilities.
3DYoga90 is organized within a three-level label hierarchy. It stands out as one of the most comprehensive open datasets, featuring the largest collection of RGB videos and 3D skeleton sequences among publicly available resources.
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Robot grasping is often formulated as a learning problem. With the increasing speed and quality of physics simulations, generating large-scale grasping data sets that feed learning algorithms is becoming more and more popular. An often overlooked question is how to generate the grasps that make up these data sets. In this paper, we review, classify, and compare different grasp sampling strategies. Our evaluation is based on a fine-grained discretization of SE(3) and uses physics-based simulation to evaluate the quality and robustness of the corresponding parallel-jaw grasps. Specifically, we consider more than 1 billion grasps for each of the 21 objects from the YCB data set. This dense data set lets us evaluate existing sampling schemes w.r.t. their bias and efficiency. Our experiments show that some popular sampling schemes contain significant bias and do not cover all possible ways an object can be grasped.
Attention Deficit Hyperactivity Disorder (ADHD) affects at least 5-10% of school-age children and is associated with substantial lifelong impairment, with annual direct costs exceeding $36 billion/year in the US. Despite a voluminous empirical literature, the scientific community remains without a comprehensive model of the pathophysiology of ADHD. Further, the clinical community remains without objective biological tools capable of informing the diagnosis of ADHD for an individual or guiding clinicians in their decision-making regarding treatment.
Precise segmentation of architectural structures provides detailed information about various building components, enhancing our understanding and interaction with our built environment. Nevertheless, existing outdoor 3D point cloud datasets have limited and detailed annotations on architectural exteriors due to privacy concerns and the expensive costs of data acquisition and annotation. To overcome this shortfall, this paper introduces a semantically-enriched, photo-realistic 3D architectural models dataset and benchmark for semantic segmentation. It features 4 different building purposes of real-world buildings as well as an open architectural landscape in Hong Kong. Each point cloud is annotated into one of 14 semantic classes.
Human Action Evaluation (HAE) has rarely been applied to real-world disease monitoring, the EHE dataset aims to gather sample data to validate effective HAE methods that could then be expanded on a larger validation scale. EHE consists of several actions from morning exercises that patients complete daily in the elderly home. The EHE dataset contained 869 action repetitions performed by 25 older people. Six exercises were collected for the EHE dataset via Kinect v2.
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