FFSE: Sonar Shadow & Scene Editing

• FFSE is an acronym used in distinct domains, denoting Fixed Focus Shadow Enhancement in sonar imaging and Free-Form Scene Editor in generative image editing.
• In sonar applications, FFSE realigns phase data in CSAS imagery to restore crisp shadow details, enabling reliable target analysis and 3D reconstruction.
• In 3D-aware editing, FFSE models object manipulation through learned 3D transformations while preserving realistic shadows, reflections, and scene consistency.

Searching arXiv for the exact FFSE-related papers to ground the article in current literature. FFSE is an acronym used in recent arXiv literature for at least two technically unrelated methods. In Circular Synthetic Aperture Sonar imaging, FFSE denotes Fixed Focus Shadow Enhancement, a post-processing phase-alignment method applied to sub-aperture CSAS images to compensate the parallax-induced shadow blur and recover a crisp projected shadow for target analysis and subsequent 3D reconstruction (Gall et al., 23 Jan 2026). In generative image editing, FFSE denotes Free-Form Scene Editor, a 3D-aware autoregressive framework for multi-round object manipulation in real-world images, designed to model editing as a sequence of learned 3D transformations while preserving physically plausible shadows, reflections, and scene consistency (Shuai et al., 17 Nov 2025). The shared acronym is therefore best understood as a case of domain-specific polysemy rather than a single unified framework.

1. FFSE as Fixed Focus Shadow Enhancement in CSAS

In the sonar literature, Fixed Focus Shadow Enhancement arises from a specific limitation of Circular Synthetic Aperture Sonar. CSAS provides a 360° azimuth view of the seabed and typically produces a very high-resolution two-dimensional image, but the parallax introduced by the circular displacement of the illuminator fill-in the shadow regions, and the shadow cast by an object on the seafloor is lost in favor of azimuth coverage and resolution (Gall et al., 23 Jan 2026). Because shadows provide complementary information on target shape useful for target recognition, FFSE is introduced as a way to retrieve shadow information from CSAS data to improve target analysis and carry 3D reconstruction.

The method is defined as a post-processing phase-alignment method applied to sub-aperture CSAS images to compensate the parallax-induced shadow blur. By “focusing” the entire sub-aperture at the known range of the target’s contact point, described as the shadow origin, FFSE realigns the phase of echoes coming from different sonar positions so that the projected shadow falls back into a single crisp silhouette (Gall et al., 23 Jan 2026). In CSAS image processing, this enables use of wider sub-apertures without suffering the loss of shadow contrast that normally occurs for angular apertures larger than approximately $5^\circ$.

A central geometric intuition is that the apparent shadow displacement varies across the circular trajectory. The data summarize this by stating that, in a circular CSAS sub-aperture of radius $R \approx$ target range, the uncorrected shifts are approximated by

$$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$

with FFSE removing the horizontal shift $\delta x$ by applying a phase ramp $h(k_x,y)$, realigning all shadows onto $x=0$ (Gall et al., 23 Jan 2026). This suggests that the method is targeted specifically at the blur mechanism induced by viewpoint-dependent shadow migration rather than at generic image sharpening.

2. Mathematical formulation and processing pipeline in sonar FFSE

The mathematical formulation is given for a complex-valued sub-aperture CSAS image $I(x,y)$, with $y_0$ the echo range of the object casting the shadow. One first defines the one-dimensional FFT along cross-range:

$F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$

For $y>y_0$, each spectral column is multiplied by a phase-correction filter

$R \approx$0

where

$R \approx$1

The filtered spectrum is then

$R \approx$2

and the shadow-enhanced image is recovered by

$R \approx$3

(Gall et al., 23 Jan 2026).

If the sub-aperture angular width is small, the formulation also permits the more exact phase filter

$R \approx$4

to account for second-order curvature (Gall et al., 23 Jan 2026). The paper further specifies a step-by-step algorithm. Starting from the full CSAS complex image $R \approx$5, one computes

$R \approx$6

builds a spectral mask $R \approx$7 for aspect angles within $R \approx$8, forms

$R \approx$9

and obtains the complex sub-aperture image

$$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$0

FFSE is then applied range-by-range for $$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$1 through $$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$2, phase filtering, and $$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$3, producing the shadow-enhanced sub-aperture image at aspect $$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$4 (Gall et al., 23 Jan 2026).

The assumptions and operating regime are narrowly stated. The method is valid for apertures up to approximately $$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$5 for mine-like objects on a locally flat seafloor, and assumes small variation in bottom topography and sonar depth across the sub-aperture (Gall et al., 23 Jan 2026). The paper reports that a sub-aperture angular width $$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$6 is typically $$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$7, while $$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$8 is used for reference.

3. Empirical role of sonar FFSE in target analysis and reconstruction

The reported performance summary is qualitative but operationally specific. Without FFSE, sub-apertures wider than approximately $$\delta x(\theta,\,A_y) \approx A_y \sin \theta,\qquad \delta y(\theta,\,A_y) \approx A_y (1-\cos \theta),$$9 produce blurred, filled-in shadows unsuitable for shape inference. Applying FFSE to a $\delta x$0 sub-aperture restores shadow sharpness to the level of a $\delta x$1 sub-aperture while retaining higher resolution (Gall et al., 23 Jan 2026). The figure summary in the data describes a three-way comparison: a $\delta x$2 aperture gives an implicitly sharp shadow but lower resolution; a $\delta x$3 aperture without FFSE exhibits strong blur in shadow; and a $\delta x$4 aperture with FFSE restores shadow clarity.

The significance of this restoration is explicit. Improved shadow clarity enables more reliable target recognition and underpins the subsequent 3D space-carving reconstruction. The broader workflow includes sub-aperture filtering to obtain a collection of images at various points of view along the circular trajectory, application of FFSE to obtain sharp shadows, an interactive interface for visualization of these shadows along the trajectory, and a space-carving reconstruction method to infer the 3D shape of the object from the segmented shadows (Gall et al., 23 Jan 2026). Qualitatively, FFSE-enabled shadows show well-defined edges and correct silhouette outlines, which the paper identifies as critical for automatic or interactive analysis in CSAS imagery.

A plausible implication is that FFSE functions not merely as an image-enhancement stage but as a geometric preconditioner for downstream inference. In the presentation given in the paper, the value of the method lies less in generic perceptual quality than in preserving shadow contrast under wider angular apertures, thereby reconciling higher spatial resolution with shadow-based shape evidence.

4. FFSE as Free-Form Scene Editor in 3D-aware image editing

In a different research area, FFSE denotes Free-Form Scene Editor, a 3D-aware autoregressive framework designed to enable intuitive, physically-consistent object editing directly on real-world images (Shuai et al., 17 Nov 2025). The method is positioned against approaches that either operate in image space or require slow and error-prone 3D reconstruction. Its central formulation is to model editing as a sequence of learned 3D transformations, allowing arbitrary manipulations such as translation, scaling, and rotation while preserving realistic background effects, including shadows and reflections, and maintaining global scene consistency across multiple editing rounds.

The framework approximates the conditional distribution of the $\delta x$5-th edited image $\delta x$6 given an edit history

$\delta x$7

via a diffusion model $\delta x$8:

$\delta x$9

where $h(k_x,y)$0 is pure noise and $h(k_x,y)$1 (Shuai et al., 17 Nov 2025). In practice, sampling proceeds by drawing $h(k_x,y)$2 and iteratively applying the learned denoiser.

The underlying operation set is given as

$h(k_x,y)$3

corresponding to translation, uniform scale, or Euler rotation about one of the object-local axes. The data present a formal view of each atomic operation as an element of an extended similarity group $h(k_x,y)$4, represented by a $h(k_x,y)$5 matrix

$h(k_x,y)$6

with $h(k_x,y)$7, $h(k_x,y)$8, and $h(k_x,y)$9 (Shuai et al., 17 Nov 2025). Composition across rounds may be written as

$x=0$0

The paper immediately qualifies this formalization by stating that FFSE does not explicitly instantiate $x=0$1 in homogeneous coordinates; instead, it encodes the relative operation parameters, injects them as network conditions, and lets the video denoiser learn to render the new view.

5. Conditioning, memory, and dataset design in Free-Form Scene Editor

A defining feature of Free-Form Scene Editor is its conditioning structure. The operation encoder is specified as

$x=0$2

where $x=0$3 is the centroid, $x=0$4 the bounding box, and $x=0$5 (Shuai et al., 17 Nov 2025). These conditions are injected into the backbone through Operation Self-Attention,

$x=0$6

and through Context Self-Attention,

$x=0$7

which enforces a learned correspondence between object pixels in round $x=0$8 and round $x=0$9 (Shuai et al., 17 Nov 2025). The latter is explicitly described as the component that ties the appearance of the same object across timesteps.

The training data are organized as a hybrid dataset

$I(x,y)$0

of edit sequences of length $I(x,y)$1 (Shuai et al., 17 Nov 2025). The real domain contains approximately $I(x,y)$2 K sequences built from RGBA foregrounds from MULAN/MS COCO with random MS COCO backgrounds, using only translation and scaling operations. The synthetic domain contains approximately $I(x,y)$3 K sequences built from panoramic HDR backgrounds from PolyHaven/Sketchfab and more than $I(x,y)$4 textured 3D models from Objaverse, allowing any of $I(x,y)$5 and rendered in Blender Cycles to obtain physically accurate shadows and reflections (Shuai et al., 17 Nov 2025).

Training proceeds in two stages, both with the standard diffusion denoising MSE loss. Stage 1 jointly fits real and synthetic data with two small domain-specific LoRA adapters:

$I(x,y)$6

and Stage 2 fine-tunes on synthetic data alone:

$I(x,y)$7

(Shuai et al., 17 Nov 2025). The paper states that no separate 3D-geometry or shadow-preservation loss is introduced; physical consistency is learned implicitly from the data and the video backbone.

At inference time, the method maintains a frame buffer $I(x,y)$8 and an operation buffer $I(x,y)$9, each holding the most recent $y_0$0 entries. When a new command $y_0$1 is issued, the operation is appended, the history $y_0$2 is formed from the paired buffers, a new frame $y_0$3 is sampled by the diffusion model, and that frame is appended to $y_0$4 (Shuai et al., 17 Nov 2025). The paper attributes multi-round consistency especially to context self-attention and the autoregressive propagation of context through the history.

6. Quantitative results, ablations, and the ambiguity of the acronym

The reported evaluation for Free-Form Scene Editor uses a pretrained image-to-video model SVD, Adam with learning rate $y_0$5, $y_0$6 A800 $y_0$7 GB GPUs, $y_0$8 resolution, batch size $y_0$9, and rounds $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$0 (Shuai et al., 17 Nov 2025). Metrics include PSNR, SSIM, DINO-Score, CLIP-Score, and a human user study over image quality, object effects, background effects, and scene consistency.

For single-round editing, the key numbers given are that FFSE achieves PSNR $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$1 dB, SSIM $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$2, DINO $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$3, and CLIP $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$4, compared with the next best baseline Zero-1-to-3 at PSNR $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$5 dB, SSIM $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$6, DINO $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$7, and CLIP $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$8 (Shuai et al., 17 Nov 2025). For six-step multi-round editing, FFSE is reported at PSNR $F(k_x,y) = \mathrm{FFT}_x\{I(x,y)\}.$9, SSIM $y>y_0$0, DINO $y>y_0$1, and CLIP $y>y_0$2, while the next best baseline reaches PSNR $y>y_0$3, SSIM $y>y_0$4, DINO $y>y_0$5, and CLIP $y>y_0$6. The user study with $y>y_0$7 raters also favors FFSE in every category, including background effects and consistency.

The ablation results isolate the role of the hybrid dataset, the two-stage training, the LoRA adapters, and the context self-attention. Training only on $y>y_0$8 yields copy-paste artifacts and missing shadows; training only on $y>y_0$9 yields over-rendered, oversaturated colors; omitting Stage 2 leaves shadows weak; omitting Domain LoRA produces coupling artifacts or failure to follow commands; and omitting Context Self-Attention causes object appearance to drift across rounds (Shuai et al., 17 Nov 2025). This suggests that the reported performance depends on a coordinated combination of data design, domain adaptation, and temporal correspondence mechanisms rather than on autoregressive diffusion alone.

Taken together, the literature shows that “FFSE” is not a single research object but a reused acronym spanning at least two specialized domains. In sonar, it refers to a phase-alignment method for restoring shadow sharpness in CSAS imagery and enabling shadow-based target analysis (Gall et al., 23 Jan 2026). In generative vision, it refers to a multi-round 3D-aware scene editor that learns physically consistent object manipulation without explicit 3D reconstruction (Shuai et al., 17 Nov 2025). The commonality is nominal rather than methodological: each addresses shadowing, geometry, and viewpoint consistency, but at entirely different levels of representation, sensing modality, and downstream purpose.