3D Depth Estimation

HUMAN4D is a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and $2$ male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc. ), along with multi-RGBD (mRGBD), volumetric and audio data. Description from: HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media Image source: https://github.com/tofis/human4d_dataset

Are current 3D object tracking methods truely robust enough for low-fidelity depth sensors like the iPhone LiDAR? We introduce DTTD-Mobile (fully compatible w/ YCB toolbox), a new benchmark built on real-world data captured from mobile devices; 18 objects observed in 100 videos with 47,668 sampled frames and 114,143 object annotations. We evaluate several popular methods—including BundleSDF, ES6D, MegaPose, and DenseFusion—and highlight their limitations in this challenging setting. Keywords: ObjectPoseEstimation, MobileAI, EdgeAI, ARVR, CVPR

TransCG is the first large-scale real-world dataset for transparent object depth completion and grasping, which contains 57,715 RGB-D images of 51 transparent objects and many opaque objects captured from different perspectives (~240 viewpoints) of 130 scenes under real-world settings. The samples are captured by two different types of cameras (Realsense D435 & L515). The following data is provided: - The 3D model of the transparent object; - The 6dpose of the transparent object in each viewpoint of each scene; - The raw RGB-D image, and the ground-truth refined depth image; - The mask of the transparent objects; - The ground-truth surface normals of every sample.

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: - High vertical resolution LiDAR with 128 channels, which is twice that of any existing datasets, full 360 degree depth, range accuracy to ±2 cm at 20-50m. - Ambient illumination (near infrared) and reflectivity panoramic imagery are made available in the Mono16 format (2048 × 128 resolution), with this being only dataset to make this provision. - No rolling shutter effect, as our flash LiDAR captures all 128 channels simultaneously. - Ambient illumination data is recorded via an on-board lux meter, which is again not available in previous datasets. - High-fidelity GNSS/INS available via an onboard OxTS navigation unit operating at 100 Hz and receiving position and timing data from multiple GNSS con-stellations in addition to GPS. - KITTI data format adopted as the de facto dataset format such that it can be parsed using both the DurLAR development kit and existing KITTI-compatible tools. - Diversity over repeated locations such that the dataset has been collected under diverse environmental and weather conditions over the same driving route with additional variations in the time of day relative to environmental conditions. ## Sensor placement - LiDAR: Ouster OS1-128 LiDAR sensor with 128 channels vertical resolution - Stereo Camera: Carnegie Robotics MultiSense S21 stereo camera with grayscale, colour, and IR enhanced imagers, 2048x1088 @ 2MP resolution - GNSS/INS: OxTS RT3000v3 global navigation satellite and inertial navigation system, supporting localization from GPS, GLONASS, BeiDou, Galileo, PPP and SBAS constellations - Lux Meter: Yocto Light V3, a USB ambient light sensor (lux meter), measuring ambient light up to 100,000 lux

Relative Human (RH) contains multi-person in-the-wild RGB images with rich human annotations, including: Depth layers: relative depth relationship/ordering between all people in the image. Age group classfication: adults, teenagers, kids, babies. Others: Genders, Bounding box, 2D pose.

A real-world dataset, with hyper-accurate digital counterpart & comprehensive ground-truth annotation. Dataset Content - 200 sequences (~400 mins) - 398 objects (324 stationary, 74 dynamic) - 2 real indoor scenes - Single + multi-user activities Sensor Data per device - 2 x outward-facing monochrome camera streams - 1 x outward-facing RGB camera stream - 2 x IMU streams - 2 x Internal-facing eye tracking cameras - Complete sensor calibrations

DRACO20K dataset is used for evaluating object canonicalization on methods that estimate a canonical frame from a monocular input image. Provides: 1. Mixed Reality Multi-view RGB-D images rendered from ShapeNet objects 2. Camera poses 3. NOCS maps 4. Semantic 2D keypoints with visibility 5. Object-centric mask

A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments.

Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. The Pano3D 360 depth estimation benchmark provides a standard Matterport3D train and test split, as well as a secondary GibsonV2 partioning for testing and training as well. The latter is used for zero-shot cross dataset transfer performance assessment and decomposes it into 3 different splits, each one focusing on a specific generalization axis.

SuperCaustics is a simulation tool made in Unreal Engine for generating massive computer vision datasets that include transparent objects. SuperCaustics is a real-time, open-source simulation of transparent objects designed for deep learning applications. SuperCaustics features extensive modules for stochastic environment creation; uses hardware ray-tracing to support caustics, dispersion, and refraction; and enables generating massive datasets with multi-modal, pixel-perfect ground truth annotations. <p align="center"> <img src="https://github.com/MMehdiMousavi/SuperCaustics/raw/main/Assets/SuperCaustics.gif" alt="drawing" width="600"/> </p>

EUEN17037 Daylight and View Standard Test Dataset.

The dataset contains procedurally generated images of transparent vessels containing liquid and objects . The data for each image includes segmentation maps, 3d depth maps, and normal maps of of the liquid or object inside the transparent vessel, and the vessel. In addition, the properties of the materials inside the containers are given(color/transparency/roughness/metalness). In addition, a natural image benchmark for the 3d/depth estimation of objects inside transparent containers is supplied. 3d models of the objects (GTLF) are also supplied.

75k photos of windows + 21k synthetic renders of building windows.