How Many Views Are Needed to Reconstruct an Unknown Object Using NeRF?

TL;DR

This work tackles the problem of inefficient NeRF-based online object reconstruction by predicting the number of views needed for a quality representation rather than relying on iterative NBV retraining. It introduces PRVNet, a ConvNeXt-V2–based regression model that maps initial viewpoint imagery to an object-specific required view count $v^*$, estimated via a curve-fitting label $v^*$ where $C_o(v+1)-C_o(v)<\alpha$. The predicted $v^*$ is used to construct a Tammes view space and compute a globally optimal Hamiltonian-path-like route, enabling fast, non-iterative data collection. Experiments on ShapeNet-generated data and real-world robot setups show PRV-Tammes achieves comparable or better PSNR/SSIM with lower movement cost and planning time than baselines, with good generalization to real environments. The approach offers a practical, scalable solution for active NeRF reconstruction in robotic applications and suggests avenues for adaptive view configurations in future work.

Abstract

Neural Radiance Fields (NeRFs) are gaining significant interest for online active object reconstruction due to their exceptional memory efficiency and requirement for only posed RGB inputs. Previous NeRF-based view planning methods exhibit computational inefficiency since they rely on an iterative paradigm, consisting of (1) retraining the NeRF when new images arrive; and (2) planning a path to the next best view only. To address these limitations, we propose a non-iterative pipeline based on the Prediction of the Required number of Views (PRV). The key idea behind our approach is that the required number of views to reconstruct an object depends on its complexity. Therefore, we design a deep neural network, named PRVNet, to predict the required number of views, allowing us to tailor the data acquisition based on the object complexity and plan a globally shortest path. To train our PRVNet, we generate supervision labels using the ShapeNet dataset. Simulated experiments show that our PRV-based view planning method outperforms baselines, achieving good reconstruction quality while significantly reducing movement cost and planning time. We further justify the generalization ability of our approach in a real-world experiment.

Figures (8)

• Figure 1: An example of how object complexity affects the required number of views to reconstruct an unknown object using NeRF. The objects are trained under 20 and 50 views of hemispherical view spaces shown in the last column. The images rendered from novel test views are shown in the first two columns. As can be seen, a less colorful and geometrically simple display can be reconstructed well with 20 views, whereas a colorful and geometrically complex flowerpot requires 50 views to achieve a good result. In this work, we present an approach to predict the required number of views by a deep neural network based on the complexity of the object to be reconstructed.
• Figure 2: An example of our online workflow given three initial views. The selected initial views (top, left, and front) are represented by red-green-blue axes. The robot takes these images and stops at the top view. We input these images into our PRVNet to obtain a predicted number of views for the reconstruction (20 in this example). Based on this, we generate the Tammes view space lai2023iterated of size 20 and the purple global path for the robot to execute.
• Figure 3: An example of quantitative analysis of the required number of views on different object complexity: (a) a simple object, (b) a complex object. Each black point is a pair of $(v, \hbox{PSNR})$, which means a NeRF trained under a view space of size $v$, and images from 100 test Tammes views are rendered to report an average PSNR value. The red curve $C_o$ is fitted to these data points to determine the $v^\ast$ based on its gradient. The blue lines suggest that for a simple object, we achieve a satisfactory result with only 20 views, whereas a complex object necessitates 40 views.
• Figure 4: PRVNet architecture: We use the state-of-the-art ConvNeXt-V2 woo2023convnext as the backbone to extract features from each image. The red arrow indicates the calculation of mean and variance across the batch dimension. $\bigoplus$ represents the concatenation operation. L1 loss is employed for network training.
• Figure 5: 3D model and required number of views datasets: (a) textured examples of the top 20 classes in ShapeNet shapenet2015; (b) training and validation set distributions over the required number of views.