# Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction

arXiv: https://arxiv.org/abs/2304.11465 · HTML: https://mlsanity.com/paper/2304.11465/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2023.

## Built on by (most cited first, 6 of 6)

- [ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis](https://mlsanity.com/paper/2409.02048/) (2024)
- [SO-NeRF: Active View Planning for NeRF using Surrogate Objectives](https://mlsanity.com/paper/2312.03266/) (2023)
- [Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction](https://mlsanity.com/paper/2603.06852/) (2026)
- [OA-NBV: Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots](https://mlsanity.com/paper/2603.11072/) (2026)
- [When to Localize? A Risk-Constrained Reinforcement Learning Approach](https://mlsanity.com/paper/2411.02788/) (2024)
- [Boundary Exploration of Next Best View Policy in 3D Robotic Scanning](https://mlsanity.com/paper/2412.10444/) (2024)

## Abstract (arXiv)

Prediction-based active perception has shown the potential to improve the navigation efficiency and safety of the robot by anticipating the uncertainty in the unknown environment. The existing works for 3D shape prediction make an implicit assumption about the partial observations and therefore cannot be used for real-world planning and do not consider the control effort for next-best-view planning. We present Pred-NBV, a realistic object shape reconstruction method consisting of PoinTr-C, an enhanced 3D prediction model trained on the ShapeNet dataset, and an information and control effort-based next-best-view method to address these issues. Pred-NBV shows an improvement of 25.46% in object coverage over the traditional methods in the AirSim simulator, and performs better shape completion than PoinTr, the state-of-the-art shape completion model, even on real data obtained from a Velodyne 3D LiDAR mounted on DJI M600 Pro.