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Neural Active Structure-from-Motion in Dark and Textureless Environment
Computer Vision and Pattern Recognition · Submitted 20 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experimental results show that the proposed method is able to achieve accurate shape reconstruction as well as pose estimation from images where only projected patterns are observed.
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The abstract, sentence by sentence
Active 3D measurement, especially structured light (SL) has been widely used in various fields for its robustness against textureless or equivalent surfaces by low light illumination. In addition, reconstruction of large scenes by moving the SL system has become popular, however, there have been few practical techniques to obtain the system's precise pose information only from images, since most conventional techniques are based on image features, which cannot be retrieved under textureless environments. In this paper, we propose a simultaneous shape reconstruction and pose estimation technique for SL systems from an image set where sparsely projected patterns onto the scene are observed (i.e. no scene texture information), which we call Active SfM. To achieve this, we propose a full optimization framework of the volumetric shape that employs neural signed distance fields (Neural-SDF) for SL with the goal of not only reconstructing the scene shape but also estimating the poses for each motion of the system. Experimental results show that the proposed method is able to achieve accurate shape reconstruction as well as pose estimation from images where only projected patterns are observed.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Active 3D measurement, especially structured light (SL) has been widely used in various fields for its robustness against textureless or equivalent surfaces by low light illumination. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | In addition, reconstruction of large scenes by moving the SL system has become popular, however, there have been few practical techniques to obtain the system's precise pose information only from images, since most conventional techniques are based on image features, which cannot be retrieved under textureless environments. | gap 0.95 | 0.05 | 0.03 | asserted 1.00 |
| s2 | In this paper, we propose a simultaneous shape reconstruction and pose estimation technique for SL systems from an image set where sparsely projected patterns onto the scene are observed (i.e. no scene texture information), which we call Active SfM. | contribution 1.00 | 0.81 | 0.12 | asserted 0.98 |
| s3 | To achieve this, we propose a full optimization framework of the volumetric shape that employs neural signed distance fields (Neural-SDF) for SL with the goal of not only reconstructing the scene shape but also estimating the poses for each motion of the system. | method 0.96 | 0.87 | 0.07 | asserted 0.98 |
| s4 | Experimental results show that the proposed method is able to achieve accurate shape reconstruction as well as pose estimation from images where only projected patterns are observed. | result 1.00 | 0.96 | 0.87 | asserted 0.94 |
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Original abstract
Active 3D measurement, especially structured light (SL) has been widely used in various fields for its robustness against textureless or equivalent surfaces by low light illumination. In addition, reconstruction of large scenes by moving the SL system has become popular, however, there have been few practical techniques to obtain the system's precise pose information only from images, since most conventional techniques are based on image features, which cannot be retrieved under textureless environments. In this paper, we propose a simultaneous shape reconstruction and pose estimation technique for SL systems from an image set where sparsely projected patterns onto the scene are observed (i.e. no scene texture information), which we call Active SfM. To achieve this, we propose a full optimization framework of the volumetric shape that employs neural signed distance fields (Neural-SDF) for SL with the goal of not only reconstructing the scene shape but also estimating the poses for each motion of the system. Experimental results show that the proposed method is able to achieve accurate shape reconstruction as well as pose estimation from images where only projected patterns are observed.
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We define rotations as rotation vectors to remove redundancy and convert them to rotation matrices to compute ray directions, as done in [ref] .
NeRF--: Neural Radiance Fields Without Known Camera Parameters · System pose estimation · 0.99
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