The Neural Regression of Scene Representation : How Depth-Line-Matrix (DLM) Reclaims the Brain’s Spatial Language for Autonomous Driving

This paper tells a story of regression to the neural organization of scene description:Since the invention of cathode-ray tube television scanning, humanity learned to describe and record scenes through 2D imaging based on pixel matrices . Although our own brains never process visual information this way [1], this fact remained unnoticed initially. Driven by Turing-inspired ingenuity , we relentlessly advanced imaging and display technologies along technically feasible paths—without biological precedents to emulate—ultimately establishing today’s ubiquitous, sophisticated imaging infrastructure that underpins information technology. Yet, escalating data volumes continue to entangle with uncertainty, particularly in autonomous driving, revealing an urgent need to revolutionize environmental perception [2]. Inspired by the artistic principle of sketching scenes with minimal lines , I propose Depth-Line-Matrix (DLM) —a novel paradigm for scene representation in autonomous driving perception. Critically, this paradigm transcends artistry; it is engineered through patented technology [3] and prior research [4]: projecting multiple pairs of parallel laser sheets with fixed spacing onto scenes generates paired stripes . Geometrically, these stripes represent intersection lines between known laser planes and scene surfaces, possessing dual attributes: Geometric signatures of scene contours Depth-encoding perspective : apparent stripe spacing Δx inversely scales with distance ( z ∝ 1/Δx ) In camera images, each stripe endpoint has pixel coordinates (x, y), while paired stripes exhibit apparent spacing Δx. Thus, I construct a virtual coordinate (x, Δx, y) to encode each endpoint. Given z = k/Δx (where k is a calibration constant), this coordinate system forms a 3D line-drawing map of the scene projected onto the 2D sensor—the Depth-Line-Matrix (DLM) . This regresses scene description from pixel-based imaging to the line-centric mechanism favored by the optic nerve (our brain) [5]. If mammalian spatial perception evolved from neural sensitivity to lines, why shouldn’t DLM evolve into spatial intelligence for machines [6-7]? I anticipate DLM will pioneer a new era of efficient environmental perception for autonomous driving.