A review of research on 3D reconstruction based on neural field inverse rendering

doi:10.11996/JG.j.2095-302X.2026030449

Abstract

Abstract:

In computer vision and computer graphics, 3D reconstruction aims to generate high-fidelity digital models of real objects and scenes from 2D images, videos, or other sensor data, and has long attracted attention from both academia and industry. In recent years, deep learning has continuously improved reconstruction accuracy and efficiency, enabling applications such as relighting, digital twins, and virtual reality. With the advent of Neural Radiance Fields (NeRF), neural-field-based reconstruction has become increasingly mainstream; inverse rendering has been incorporated to enhance appearance disentanglement, forming a more complete reconstruction framework. Motivated by a rapidly growing yet fragmented literature, this survey focused on static objects and scenes and organized neural-field-based inverse-rendering 3D reconstruction into two components: neural-field surface reconstruction, and neural-field material and illumination estimation. For each component, core research questions were distilled and representative methods were summarized following a “research problem-technical solution” perspective. Their key principles and derivations were analyzed to inform future algorithm design. In addition, commonly used datasets and evaluation metrics for both directions were compiled, and their practical details and applicability were discussed. Finally, open challenges and promising future directions were outlined.

Key words: 3D reconstruction, neural radiance field, inverse rendering, neural fields, surface reconstruction, material illumination estimation

CLC Number:

TP391.41

ZHOU Xueyang, SHEN Xukun, HU Yong. A review of research on 3D reconstruction based on neural field inverse rendering[J]. Journal of Graphics, 2026, 47(3): 449-471.

Figures/Tables 22

Fig. 1 Schematic diagram of 3D reconstruction process based on inverse rendering

Fig. 2 Research problems in 3D reconstruction based on neural field inverse rendering

Fig. 3 The pipeline of NeRF[6]

Fig. 4 Classification tree of “problem-technical solution” based on neural field for surface reconstruction

Fig. 5 Schematic diagram of implicit displacement fields[22]

Fig. 6 Schematic diagram of MonoSDF[30]

Fig. 7 Comparison diagram of Ref-NeRF[37]

Fig. 8 Schematic diagram of Ref-NeuS[46]

Fig. 9 Schematic diagram of TensoSDF[53]

Fig. 10 Schematic diagram of LTM[61]

Fig. 11 Schematic diagram of MonoGSDF[73]

Fig. 12 Schematic diagram of frequency control process for FourieRF[78]

Fig. 13 Schematic diagram of NGLoD[80]

Fig. 14 Schematic diagram of MFLOD[84]

Fig. 15 Classification tree of “problem-technical solution” based on neural field for material illumination estimation

Fig. 16 Schematic diagram of TensoIR[95]

Fig. 17 Schematic diagram of InvRender[101]

Fig. 18 Schematic diagram of NDE[108]

Fig. 19 Schematic diagram of the sampler of TensoFlow[114]

Fig. 20 Schematic diagram of PIR[118]

Fig. 21 Schematic diagram of VQ-NeRF[125]

Fig. 22 Schematic diagram of neural field conditioning

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