12 dataset results for Surface Normals Estimation

ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects. Up to now, ScanNet v2, the newest version of ScanNet, has collected 1513 annotated scans with an approximate 90% surface coverage. In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects.

1,240 PAPERS • 19 BENCHMARKS

NYUv2 (NYU-Depth V2)

The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It features:

841 PAPERS • 20 BENCHMARKS

PASCAL Context

The PASCAL Context dataset is an extension of the PASCAL VOC 2010 detection challenge, and it contains pixel-wise labels for all training images. It contains more than 400 classes (including the original 20 classes plus backgrounds from PASCAL VOC segmentation), divided into three categories (objects, stuff, and hybrids). Many of the object categories of this dataset are too sparse and; therefore, a subset of 59 frequent classes are usually selected for use.

278 PAPERS • 6 BENCHMARKS

Taskonomy

Taskonomy provides a large and high-quality dataset of varied indoor scenes.

132 PAPERS • 2 BENCHMARKS

IBims-1 (Independent benchmark images and matched scans v1)

iBims-1 (independent Benchmark images and matched scans - version 1) is a new high-quality RGB-D dataset, especially designed for testing single-image depth estimation (SIDE) methods. A customized acquisition setup, composed of a digital single-lens reflex (DSLR) camera and a high-precision laser scanner was used to acquire high-resolution images and highly accurate depth maps of diverse indoors scenarios.

24 PAPERS • 2 BENCHMARKS

GRIT (General Robust Image Task Benchmark)

The General Robust Image Task (GRIT) Benchmark is an evaluation-only benchmark for evaluating the performance and robustness of vision systems across multiple image prediction tasks, concepts, and data sources. GRIT hopes to encourage our research community to pursue the following research directions:

13 PAPERS • 8 BENCHMARKS

Stanford-ORB

We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark. Recent advances in inverse rendering have enabled a wide range of real-world applications in 3D content generation, moving rapidly from research and commercial use cases to consumer devices. While the results continue to improve, there is no real-world benchmark that can quantitatively assess and compare the performance of various inverse rendering methods. Existing real-world datasets typically only consist of the shape and multi-view images of objects, which are not sufficient for evaluating the quality of material recovery and object relighting. Methods capable of recovering material and lighting often resort to synthetic data for quantitative evaluation, which on the other hand does not guarantee generalization to complex real-world environments. We introduce a new dataset of real-world objects captured under a variety of natural scenes with ground-truth 3D scans, multi-view images, and environment l

9 PAPERS • 5 BENCHMARKS

3D Ken Burns Dataset

Provides a large-scale synthetic dataset which contains accurate ground truth depth of various photo-realistic scenes.

3 PAPERS • NO BENCHMARKS YET

Pano3D

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.

2 PAPERS • NO BENCHMARKS YET

SuperCaustics

SuperCaustics is a simulation tool made in Unreal Engine for generating massive computer vision datasets that include transparent objects.

2 PAPERS • 1 BENCHMARK

SynFoot

50K synthetic renders of the human foot, with surface normals, masks and keypoints.

1 PAPER • NO BENCHMARKS YET

TransProteus

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.

1 PAPER • 1 BENCHMARK

Datasets

12 dataset results for Surface Normals Estimation