EmerNeRF

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EmerNeRF is a Research & Education tool that provides a PyTorch implementation for emergent spatial-temporal scene decomposition. It enables self-supervised motion estimation and 4D feature lifting for scene understanding.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is EmerNeRF?

EmerNeRF offers a self-supervised approach for spatial-temporal scene decomposition using neural fields. It can effectively separate dynamic objects from a static background and estimate their motion without explicit supervision. The tool also enriches 2D features by lifting and 'denoising' them in 4D space-time, opening new possibilities for advanced scene understanding. EmerNeRF supports the NeRF On-The-Road (NOTR) dataset, derived from the Waymo Open Dataset, and NuScenes, with provisions for custom dataset integration. It is implemented in PyTorch and designed for researchers and developers working on neural radiance fields and 3D scene reconstruction.

Best used for

Ideal for professors and researchers who need to explore emergent spatial-temporal scene decomposition, estimate motion in complex environments, and lift foundation features for enhanced scene understanding. Especially valuable for those working with neural radiance fields and large-scale driving datasets like Waymo and NuScenes.

Common actions

decompose scenes

estimate motion

lift features

research neural fields

develop self-supervised models

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Capabilities

Key features

Self-supervised scene decomposition
Motion estimation
4D feature lifting
NOTR dataset support
NuScenes dataset support
PyTorch implementation

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What datasets does EmerNeRF support for scene decomposition?

EmerNeRF currently supports the NeRF On-The-Road (NOTR) dataset, which is derived from the Waymo Open Dataset, and the NuScenes dataset. Users can also adapt their custom datasets by using these two as templates for preparation within the codebase.

What are the system requirements for running EmerNeRF?

The EmerNeRF code is developed and tested on Ubuntu 22.04, using Python 3.9 and PyTorch 2.0. It is recommended to use conda for dependency installation, and the process may take over 30 minutes due to various package installations.

How can I visualize the spatial-temporal features generated by EmerNeRF?

EmerNeRF provides visualization code to display spatial-temporal features as voxels. By adding the `--visualize_voxel` flag and specifying a pretrained model, an HTML file will be generated, which can be opened in a browser to inspect the voxel features.

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Content & Design › 3D & Animation AI Agents & Automation › AI Frameworks & Infra

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