ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toyota Technological Institute at Chicago, USA. The repository contains over 300M models with 220,000 classified into 3,135 classes arranged using WordNet hypernym-hyponym relationships. ShapeNet Parts subset contains 31,693 meshes categorised into 16 common object classes (i.e. table, chair, plane etc.). Each shapes ground truth contains 2-5 parts (with a total of 50 part classes).
1,689 PAPERS • 13 BENCHMARKS
OmniObject3D is a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties:
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We present a large-scale dataset for 3D urban scene understanding. Compared to existing datasets, our dataset consists of 75 outdoor urban scenes with diverse backgrounds, encompassing over 15,000 images. These scenes offer 360◦ hemispherical views, capturing diverse foreground objects illuminated under various lighting conditions. Additionally, our dataset encompasses scenes that are not limited to forward-driving views, addressing the limitations of previous datasets such as limited overlap and coverage between camera views. The closest pre-existing dataset for generalizable evaluation is DTU [2] (80 scenes) which comprises mostly indoor objects and does not provide multiple foreground objects or background scenes.
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The new dataset contains around 1,500 train videos and 290 test videos, with 50 frames per video on average. The dataset was obtained after processing the manually captured video sequences of static real-life urban scenes. The main property of the dataset is the abundance of close objects and, consequently, the larger prevalence of occlusions. According to the introduced heuristic, the mean area of occluded image parts for SWORD is approximately five times larger than for RealEstate10k data (14% vs 3% respectively). This rationalizes the collection and usage of SWORD and explains that SWORD allows training more powerful models despite being of smaller size.
Synthetic dataset comprising three different environments for multi-camera dynamic novel view synthesis for soccer. This dataset is made compatible for Nerfstudio, and includes data parsers with various settings to reproduce the settings of our paper "Dynamic NeRFs for Soccer Scenes" and more.
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Dataset of paired thermal and RGB images comprising ten diverse scenes—six indoor and four outdoor scenes— for 3D scene reconstruction and novel view synthesis (e.g. with NeRF).