Reliability-Aware Reconstruction of V-Shaped Surfaces with a Low-Cost Multi-Zone SPAD Sensor

Yohanssen Pratama

cs.CVOct 20, 2026

Reliability-Aware Reconstruction of V-Shaped Surfaces with a Low-Cost Multi-Zone SPAD Sensor

Authors: Yohanssen Pratama

Organizations: Institute for International Strategy and Emerging Technologies, Tokyo International University, 1-13-1 Matoba-Kita, Kawagoe, 350-1197, Japan

Abstract

Low-cost multi-zone single-photon avalanche diode (SPAD) sensors provide compact time-of-flight measurements but are limited by sparse sampling and unreliable depth estimates near geometric discontinuities. This letter presents a reliability-aware reconstruction framework for V-shaped surfaces using two complementary TMF8821 acquisition configurations. Point-wise reliability is learned from sensor confidence, transient peak amplitude, and peak sharpness, and is incorporated into a graph-based depth optimization. The reconstructed geometry is evaluated by robust two-plane fitting and a perpendicular cross-section. Experiments on 60-degree, 90-degree, and 120-degree targets reduce the mean V-angle error from 11.21 degrees to 9.66 degrees (13.8%). For the representative 90-degree target, the combined point-to-plane RMSE decreases from 14.56 mm to 13.65 mm.

Figures & tables

Figure 1: Overview of the proposed reliability-aware V-shaped surface reconstruction pipeline.

Figure 2: Spatial sampling patterns of the custom TMF8821 configurations: (a) Configuration B and (b) horizontally shifted Configuration C.

Figure 3: Transient feature extraction and point-wise reliability: (a) representative transient histogram and (b) normalized features and learned reliability $R_{i}$ .

Figure 4: V-shaped geometry estimation: (a) fitted 3-D planes and (b) perpendicular cross-section for the opening angle $\theta$ .

Figure 5: Experimental setup: (a) TMF8821-to-target arrangement and (b) TMF8821 multi-zone dToF sensor module.

GT	Before optimization		After optimization
GT	Est.	Error	Est.	Error
$60^{\circ}$	$66.42^{\circ}$	$6.42^{\circ}$	$65.21^{\circ}$	$5.21^{\circ}$
$90^{\circ}$	$98.91^{\circ}$	$8.91^{\circ}$	$97.45^{\circ}$	$7.45^{\circ}$
$120^{\circ}$	$138.29^{\circ}$	$18.29^{\circ}$	$136.31^{\circ}$	$16.31^{\circ}$
Mean	–	$\mathbf{11.21^{\circ}}$	–	$\mathbf{9.66^{\circ}}$

Table 1: V-angle reconstruction accuracy before and after optimization.

Figure 6: V-angle reconstruction before and after optimization for $60^{\circ}$ , $90^{\circ}$ , and $120^{\circ}$ targets.

Figure 7: Geometric reconstruction quality for the $90^{\circ}$ V-shaped target before and after reliability-aware optimization.

Figure 8: Ablation on the $90^{\circ}$ target: (a) one configuration before optimization, (b) two aggregated configurations before optimization, and (c) two configurations after the proposed reliability-aware optimization.

Method	Points	Est. angle	Error
Config. B only	93	$102.32^{\circ}$	$12.32^{\circ}$
B+C raw	176	$98.91^{\circ}$	$8.91^{\circ}$
B+C proposed	176	$97.45^{\circ}$	$7.45^{\circ}$

Table 2: Multi-configuration ablation on the

90^{\circ}

target.

Depth Estimation

Depth estimation infers scene distance from images and sensors, enabling 3D reconstruction, navigation, and spatial understanding.

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Jan 12, 2026J/K move · Enter open · S save

Fundamental Recovery Bounds for SPAD Signals under Stationary Flux

Lior Dvir, Nadav Torem, Mohit Gupta, Yoav Y. Schechner

Viterbi Faculty of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel · Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA

Bayesian Inverse Problems Diffusion Posterior Sampling Inverse Problems

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Single-photon avalanche diodes (SPADs) record light as a discrete stream of individual detections. The signal is stochastic. Its statistical structure depends on the sensor's operation mode: binary detection in fixed bins, timestamped detection in fixed bins, or free-running timestamped detection. We derive the likelihood score function for each of these three passive modes. From this single object, stem both fundamental limits of recovery (Cramer-Rao bounds) and practical recovery algorithms based on diffusion posterior sampling. The paper further generalizes fundamental limits to Bayesian Cramer-Rao lower bounds. This generalization makes use of a learned approximation of the score function of signal priors. In prior art, analyses and diffusion-based reconstruction for SPAD data have treated individual modes in isolation. Our unified treatment shows a qualitative high-flux gap between modes: binary counts saturate exponentially, while timestamped modes degrade only linearly. We further extend diffusion posterior sampling, previously restricted to binary SPAD data, to a full timestamped case using the suitable score function. We demonstrate experimentally that matching the score to the operation mode is beneficial for high-fidelity reconstruction. By tying the recovery bounds and diffusion to the score function, this work aims to establish a common foundation for both asking what is recoverable in single-photon sensing, and building methods that approach the bound.

Abstract

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