To View Transform or Not to View Transform: NeRF-based Pre-training Perspective

• URL: http://arxiv.org/abs/2603.28090v1
• Date: Mon, 30 Mar 2026 06:46:34 GMT
• Title: To View Transform or Not to View Transform: NeRF-based Pre-training Perspective
• Abstract summary: We propose a novel NeRF-Resembled Point-based 3D detector that can learn continuous 3D representation and thus avoid the misaligned priors from view transformation.<n>NeRP3D preserves the pre-trained NeRF network regardless of the tasks, inheriting the principle of continuous 3D representation learning.
• Score: 15.748407383575604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural radiance fields (NeRFs) have emerged as a prominent pre-training paradigm for vision-centric autonomous driving, which enhances 3D geometry and appearance understanding in a fully self-supervised manner. To apply NeRF-based pretraining to 3D perception models, recent approaches have simply applied NeRFs to volumetric features obtained from view transformation. However, coupling NeRFs with view transformation inherits conflicting priors; view transformation imposes discrete and rigid representations, whereas radiance fields assume continuous and adaptive functions. When these opposing assumptions are forced into a single pipeline, the misalignment surfaces as blurry and ambiguous 3D representations that ultimately limit 3D scene understanding. Moreover, the NeRF network for pre-training is discarded during downstream tasks, resulting in inefficient utilization of enhanced 3D representations through NeRF. In this paper, we propose a novel NeRF-Resembled Point-based 3D detector that can learn continuous 3D representation and thus avoid the misaligned priors from view transformation. NeRP3D preserves the pre-trained NeRF network regardless of the tasks, inheriting the principle of continuous 3D representation learning and leading to greater potentials for both scene reconstruction and detection tasks. Experiments on nuScenes dataset demonstrate that our proposed approach significantly improves previous state-of-the-art methods, outperforming not only pretext scene reconstruction tasks but also downstream detection tasks.

Related papers

• Visual Geometry Transformer in the Wild: Distractor-Free 3D Reconstruction [77.75911117889915]
  Current end-to-end multi-view 3D reconstruction methods achieve impressive results, but rely on a restrictive static assumption.<n>This reliance on idealized inputs causes even the most advanced methods to fail in real-world settings.<n>We propose Visual Geometry Transformer in the Wild, an end-to-end framework for robust reconstruction from inconsistent views.
  arXiv  Detail & Related papers  (2026-06-22T02:52:22Z)
• 3D Visibility-aware Generalizable Neural Radiance Fields for Interacting Hands [51.305421495638434]
  Neural radiance fields (NeRFs) are promising 3D representations for scenes, objects, and humans. This paper proposes a generalizable visibility-aware NeRF framework for interacting hands. Experiments on the Interhand2.6M dataset demonstrate that our proposed VA-NeRF outperforms conventional NeRFs significantly.
  arXiv  Detail & Related papers  (2024-01-02T00:42:06Z)
• Registering Neural Radiance Fields as 3D Density Images [55.64859832225061]
  We propose to use universal pre-trained neural networks that can be trained and tested on different scenes. We demonstrate that our method, as a global approach, can effectively register NeRF models.
  arXiv  Detail & Related papers  (2023-05-22T09:08:46Z)
• Clean-NeRF: Reformulating NeRF to account for View-Dependent Observations [67.54358911994967]
  This paper proposes Clean-NeRF for accurate 3D reconstruction and novel view rendering in complex scenes. Clean-NeRF can be implemented as a plug-in that can immediately benefit existing NeRF-based methods without additional input.
  arXiv  Detail & Related papers  (2023-03-26T12:24:31Z)
• NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions [97.27105725738016]
  integration of Neural Radiance Fields (NeRFs) and generative models, such as Generative Adversarial Networks (GANs) has transformed 3D-aware generation from single-view images. We propose a simple and effective method, based on re-using the well-disentangled latent space of a pre-trained NeRF-GAN in a pose-conditioned convolutional network to directly generate 3D-consistent images corresponding to the underlying 3D representations.
  arXiv  Detail & Related papers  (2023-03-22T18:59:48Z)
• NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion [107.67277084886929]
  Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the input. We propose NerfDiff, which addresses this issue by distilling the knowledge of a 3D-aware conditional diffusion model (CDM) into NeRF through synthesizing and refining a set of virtual views at test time. We further propose a novel NeRF-guided distillation algorithm that simultaneously generates 3D consistent virtual views from the CDM samples, and finetunes the NeRF based on the improved virtual views.
  arXiv  Detail & Related papers  (2023-02-20T17:12:00Z)
• Learning Neural Radiance Fields from Multi-View Geometry [1.1011268090482573]
  We present a framework, called MVG-NeRF, that combines Multi-View Geometry algorithms and Neural Radiance Fields (NeRF) for image-based 3D reconstruction. NeRF has revolutionized the field of implicit 3D representations, mainly due to a differentiable rendering formulation that enables high-quality and geometry-aware novel view synthesis.
  arXiv  Detail & Related papers  (2022-10-24T08:53:35Z)
• Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations [111.08941206369508]
  We propose Augmented NeRF (Aug-NeRF), which for the first time brings the power of robust data augmentations into regularizing the NeRF training. Our proposal learns to seamlessly blend worst-case perturbations into three distinct levels of the NeRF pipeline. Aug-NeRF effectively boosts NeRF performance in both novel view synthesis and underlying geometry reconstruction.
  arXiv  Detail & Related papers  (2022-07-04T02:27:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.