mlsanityData to 14 Oct 2026

Evaluate Geometry of Radiance Fields with Low-frequency Color Prior

arXiv 2304.04351 ↗First posted 10 Apr 2023cs.CVcited by 2

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Abstract (arXiv)

A radiance field is an effective representation of 3D scenes, which has been widely adopted in novel-view synthesis and 3D reconstruction. It is still an open and challenging problem to evaluate the geometry, i.e., the density field, as the ground-truth is almost impossible to obtain. One alternative indirect solution is to transform the density field into a point-cloud and compute its Chamfer Distance with the scanned ground-truth. However, many widely-used datasets have no point-cloud ground-truth since the scanning process along with the equipment is expensive and complicated. To this end, we propose a novel metric, named Inverse Mean Residual Color (IMRC), which can evaluate the geometry only with the observation images. Our key insight is that the better the geometry, the lower-frequency the computed color field. From this insight, given a reconstructed density field and observation images, we design a closed-form method to approximate the color field with low-frequency spherical harmonics, and compute the inverse mean residual color. Then the higher the IMRC, the better the geometry. Qualitative and quantitative experimental results verify the effectiveness of our proposed IMRC metric. We also benchmark several state-of-the-art methods using IMRC to promote future related research. Our code is available at https://github.com/qihangGH/IMRC.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 43 references that are in the map. First posted on arXiv 10 Apr 2023.

Builds on

23 of 43 references.

References · 23

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  2. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
  3. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  4. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,232
  5. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 964
  6. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 861
  7. 2021MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereocited by 740
  8. 2021IBRNet: Learning Multi-View Image-Based Renderingcited by 703
  9. 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 673
  10. 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
  11. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
  12. 2021FastNeRF: High-Fidelity Neural Rendering at 200FPScited by 511
11 more
  1. 2022HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Videocited by 483
  2. 2021Baking Neural Radiance Fields for Real-Time View Synthesiscited by 468
  3. 2021Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesiscited by 464
  4. 2021NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Imagescited by 336
  5. 2022NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstructioncited by 314
  6. 2021NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wildcited by 136
  7. 2022Mixed Neural Voxels for Fast Multi-view Video Synthesiscited by 127
  8. 2021HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputscited by 112
  9. 2022Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstructioncited by 94
  10. 2022Improved Direct Voxel Grid Optimization for Radiance Fields Reconstructioncited by 61
  11. 2021DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Renderingcited by 52

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.