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Related Experiment Video

Updated: May 11, 2026

Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

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Published on: November 21, 2016

OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments.

Chubin Zhang, Juncheng Yan, Yi Wei

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 14, 2025

Summary

This study introduces OccNeRF, a novel method for 3D occupancy prediction without requiring LiDAR data. It enables vision-based systems to reconstruct environments accurately for autonomous driving.

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Area of Science:

Computer Vision
Robotics
3D Reconstruction

Background:

Occupancy prediction is crucial for autonomous driving, but current methods often depend on LiDAR data.
Vision-based systems lack the 3D ground truth typically generated from LiDAR point clouds.

Purpose of the Study:

To develop a method for training occupancy networks without 3D ground truth.
To enable accurate 3D environment reconstruction using only camera data for autonomous navigation.

Main Methods:

OccNeRF parameterizes occupancy fields and adapts sampling for cameras' infinite range.
Neural rendering converts occupancy fields to depth maps, supervised by photometric consistency.
Strategies were developed to refine semantic predictions from pre-trained 2D segmentation models.

Main Results:

The method effectively performs self-supervised depth estimation and 3D occupancy prediction.
Experiments on nuScenes and SemanticKITTI datasets validate the approach's effectiveness.
OccNeRF demonstrates robust performance in vision-based 3D scene reconstruction.

Conclusions:

OccNeRF successfully trains occupancy networks without 3D ground truth, overcoming limitations of LiDAR dependency.
The proposed method enhances autonomous driving capabilities by enabling accurate 3D perception from visual data.
This work paves the way for more accessible and versatile 3D reconstruction in autonomous systems.