Self-supervised pretraining of 3d features on any point-cloud
Pretraining on large labeled datasets is a prerequisite to achieve good performance in many
computer vision tasks like image recognition, video understanding etc. However, pretraining …
computer vision tasks like image recognition, video understanding etc. However, pretraining …
3d-front: 3d furnished rooms with layouts and semantics
Abstract We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a
new, large-scale, and compre-hensive repository of synthetic indoor scenes highlighted by …
new, large-scale, and compre-hensive repository of synthetic indoor scenes highlighted by …
MIME: Human-aware 3D scene generation
H Yi, CHP Huang, S Tripathi, L Hering… - Proceedings of the …, 2023 - openaccess.thecvf.com
Generating realistic 3D worlds occupied by moving humans has many applications in
games, architecture, and synthetic data creation. But generating such scenes is expensive …
games, architecture, and synthetic data creation. But generating such scenes is expensive …
Atiss: Autoregressive transformers for indoor scene synthesis
D Paschalidou, A Kar, M Shugrina… - Advances in …, 2021 - proceedings.neurips.cc
The ability to synthesize realistic and diverse indoor furniture layouts automatically or based
on partial input, unlocks many applications, from better interactive 3D tools to data synthesis …
on partial input, unlocks many applications, from better interactive 3D tools to data synthesis …
Lego-net: Learning regular rearrangements of objects in rooms
Humans universally dislike the task of cleaning up a messy room. If machines were to help
us with this task, they must understand human criteria for regular arrangements, such as …
us with this task, they must understand human criteria for regular arrangements, such as …
Planit: Planning and instantiating indoor scenes with relation graph and spatial prior networks
We present a new framework for interior scene synthesis that combines a high-level relation
graph representation with spatial prior neural networks. We observe that prior work on scene …
graph representation with spatial prior neural networks. We observe that prior work on scene …
Sceneformer: Indoor scene generation with transformers
X Wang, C Yeshwanth… - … Conference on 3D Vision …, 2021 - ieeexplore.ieee.org
We address the task of indoor scene generation by generating a sequence of objects, along
with their locations and orientations conditioned on a room layout. Large-scale indoor scene …
with their locations and orientations conditioned on a room layout. Large-scale indoor scene …
Fast and flexible indoor scene synthesis via deep convolutional generative models
We present a new, fast and flexible pipeline for indoor scene synthesis that is based on
deep convolutional generative models. Our method operates on a top-down image-based …
deep convolutional generative models. Our method operates on a top-down image-based …
Fov-nerf: Foveated neural radiance fields for virtual reality
Virtual Reality (VR) is becoming ubiquitous with the rise of consumer displays and
commercial VR platforms. Such displays require low latency and high quality rendering of …
commercial VR platforms. Such displays require low latency and high quality rendering of …
Advances in Data‐Driven Analysis and Synthesis of 3D Indoor Scenes
This report surveys advances in deep learning‐based modelling techniques that address
four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We …
four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We …