Contrastive Diffusion Guidance for Spatial Inverse Problems
Authors: Sattwik Basu
Chaitanya Amballa
Zhongweiyang Xu
Jorge Vančo Sampedro
Srihari Nelakuditi
Romit Roy Choudhury
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Models
Inverse Problems
Contrastive Learning
Spatial Inference
Primary area: generative models

Abstract: We consider a class of inverse problems characterized by forward operators that are partially specified, non-smooth, and non-differentiable.
Although generative inverse solvers have made significant progress, we find that these forward operators introduce a distinct set of challenges.
As a concrete instance, we consider the problem of reconstructing spatial layouts, such as floorplans, from human movement trajectories, where the underlying path-generation process is inherently non-differentiable and only partially known.
In such problems, direct likelihood-based guidance becomes unstable, since the underlying path-planning process does not provide reliable gradients.
We break-away from existing diffusion-based posterior samplers and reformulate likelihood-based guidance in a smoother embedding space.
This embedding space is learned using a contrastive objective to bring compatible trajectory-floorplan pairs close together while pushing mismatched pairs apart.
We show that this surrogate likelihood score in the embedding space provides a valid approximation to the true likelihood score, making it possible to steer the denoising process towards the posterior.
Across extensive experiments, our model CoGuide produces more consistent reconstructions and is more robust than existing inverse-solvers and guided diffusion.
Beyond spatial mapping, we show that our method can be applied more broadly, suggesting a route toward solving generalized blind inverse problems using diffusion models.

Published as a conference paper at ICLR 2026
CONTRASTIVE DIFFUSION GUIDANCE FOR SPATIAL INVERSE PROBLEMS
Sattwik Basu
Chaitanya Amballa'
Zhongweiyang Xu' Romit Roy Choudhury'
Jorge Vančo Sampedro'
Srihari Nelakuditi
University of Illinois Urbana- Champaag Uniiveriiyy of South Carolina
ABSTRACT
We consider a class of inverse problems characterized by forward operators that are partially specified, non-smooth, and non-differentiable. Although generative inverse solvers have made significant progress, find that these forward operators introduce a distinct set of challenges. As a concrete instance, we consider the problem reconstructing spatial layouts, such as floorplans, from human movement trajectories, where the underlying path-generation process is inherently non-differentiable and only partially known. In such problems, direct likelihood-based guidance becomes unstable, since the underlying path-planning process does not provide reliable gradients. We break- away from existing diffusion- based posterior samplers and reformulate likelihood- baeeehood-based guidance in smoother embedding space. This embedding space is pairs close
learned using contrastive objective bring compatible trajectory-floone together while pushing mismatched pairs apart. We show that this surrogate likelihood score in the embedding space provides a valid approximation to the true likelihood score, making it possible to steer the denoising process towards the posterior. Across extensive experiments, our model CoGuide produces more consistent reconstructions and is more robust than existing inverse-solve and guided diffusion. Beyond spatial mapping, we show that our method can be applied more broadly, suggesting a route toward solving generalized blind inverse problems using diffusion models.
INTRODUCTION
Inverse problems (IP) seek to recover unknown signals from indirect, partial, and often noisy mcasurements. The unknown signal R and the measurement y RR' related via forward process = A=x.xx)), where A:R.. - is a forward operator and n denotes the measurement noise. The objective is to estimate when given only the observation y, that is, construct suitable inverse map At such that A' (y).
The fundamental challenge in inverse problems is ill-posedness/ Hadamard & Morse (1533) which neces- sitates the use of structural priors. Past work has made remarkable progress using insightful observations on the nature of x, leading to hand-crafted priors like sparsity, total variation, etc. Engl et al. (1996). These priors make optimization tractable (c.g., maximum a posteriori) but often underfit complex structure and need careful tuning to balance fidelity and regularization. In recent years, diffusion models SShh--Dickster al. (2015); [Song al. (2020b); Ho et al. (2020) have become a powerful line of attack since they can extract priors from large datasets, and use the prior to sample from the posterior distribution Zheng et al. (2025)/Chhnng et al. (2022); Song et al. (2023). Active progress is being made along the axis of operator complexity Stnptexity-sttrtiingg from linear and non-linear operators, and going into non-differentiable, partially ob- servable, and even blind functions. This paper brings forth a reasonably challenging (path-plann paannnnnnnnnn) operator, motivated by practical application. Let us present the application first and then shed light the operator.
Consider a user walking around in her home for a few minutes. Using some sensor, e.g., a smartphone, the user's trajectory has been recorded. This trajectory is sequence of location measurements inside the home, y= (/..../.... along which the user has walked. We ask, given this trajectory measurement, it possible to infer the floorplan of the home, where floorplan the dimensions and layouts of the walls in the home. Observe that this is a spatial inverse problem, modeled as y = A(x,n), because the way the human user walks, y, is indeed function of the layout of the home x. This function is the A(.) operator, a policy in the user's brain that plans the path from point A to point B, for a given floorplan
"Equal contribution.

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Published as a conference paper at ICLR 2026
This path path-planner is complex- it models factors such distance walked, collision with walls and furniture, time to walk, number of turns, etc. Optimizations with such functions inherit this complexity since tiny changes in the floorplan -ay small hole in one wall-can drastically change the planned path. Hence, A(.) is in the regime of non-linear, non-differentiable, and partially observable operators, presenting relatively new question (to the best of our knowledge) diffusion-based inverse solvers. This paper concentrates on this specific problem of inverse floorplan estimation, but shows the potential to extend the approach to broader family of spatial inverse problems.
Our key idea follows score-based posterior sampling in which the posterior score V Vxlogp(x| y decom- poses into a diffusion-leamed prior score Vxlog p(x) and a likelihood score Vxlog p(y]x) Song et al. (2000b). The prior tern injects structural knowledge about plausible x. while the likelihood term enforces consistency with the measurement, y. Following Diffusion Posterior Sampling (DPS) Chung et al. (2022). we approximate the likelihood score as Vx log p(y (8a) using Tweedie's 's mean estimate Xy = E. E X4
Efron 12. When A(.) is known and differentiable with additive n ~~ the likelihood score and provides guidance for the denoising process in diffusion. In our case, the path planning operator A(.) is difficult to model and non-smooth, and as we show, various approximations of A(.)) even when differentiable proluleeerruuceeeee poor results due to the instability in optimization.
reduces to Vxlly ((xo) [2
In light of this, we break-away from convention and project both, floorplans x and trajectory y, into a common embedding space E, in which the likelihood score assumes a surrogate form:
Vx
[ -
where E. E We train this embedding space using a contrastive approach Jaiswal et al. (202)) 1 Le- Khac al. (2020) that pulls matching (trajectory, floorplan) pairs closer to each other. and pushes away pairs that are incompatble. In other words, the embedding space implicitly learns the A(.) operator from matching pairs of floorplan and trajectory data, where the latter is synthetically generated from the former using an approximate A(.) operator. Importantly, the likelihood term in the embedding space is a smoother function for optimization and we show that it is a valid surrogate of the original intractable likelihood score.
We train our Diffusion prior using public floorplan datasets; during inference, our method CoGuide generates floorplans for given (sparse, medium, or dense) trajectories. Results reliably outperform different baselines: 3 that are augmentations of DPS with path-planners Yonetani et al. (2021); Kirilenko cet al. )0023); Liu et al. (2024), 2 that are established inverse solvers Zhhu et al. (2023): [Wang et al. (2024), and classifier free guidance (CFG) diffusion model & Salimans (2022) that (over)fits to the joint distribution of trajectories and floorplans. We believe CoGuide has potential beyond this specific application of floorplan inference and demonstrate generalization through blind audio restoration (Appendix D) an inverse problem where the forward degradation operator is completely unknown. We discuss early thoughts on follow-on research directions to leverage contrastive leaming in diffusion- inverse solvers.
2 PRELIMINARIES
Diffusion models Dickstiic et al. (2015) Ho et al. 2020 Song et al. 2020a b are a class of generative models capable of producing high-quality samples across wide range of domains, including images Dhariwal & Nichol (202I). audio Kong et al. (2020b); Liu et al. (2023), video Ho et al. (2022). and 3D data Luo & Hu (2021): Poole et al. (2022). These models define generation as the reversal of a forward noising process that is formalized using a stochastic differential equation (SDE).
dx, dx;= (xx=]((xx))dl++=(()))))))
where x, E Rd is the state time e [0.7]] fxx,,t) is the drift, g(t) is the diffusion coefficient, and w; is a standard Brownian motion (Wiener process). Starting from clean data X ~ Paam. this forward process gradually comupts the signal so that XT (0.I). The associated reverse-time SDE, derived by Anderson 1982), recovers data from noise:
2V logns +g())
where Pe (xx) is the marginal density of x, time t, Vx, lo.glo(p)))) is the (time-dependent) score function, and w, is reverse-time time Brownian motion.
Inverse Solvers. Diffusion models have also been adapted for inverse problems based on the insight that one can design a reverse SDE using the posterior score as:
V
x, log (xxly) V xx, (xg))(x()
log
posterior soute
prior SCOTS
likelihood scure
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Here, the prior score is easy to approximate by trained diffusion model (x))).)). The likelihood score, in contrast, is intractable as y depends only on Xp. not directly on Xt. In response to this, prior work such as Diffusion Posterior Sampling (DPS) Chung et al. (2022) have approximated the likelihood term as p:(y)x,) R p(y|Xn) where Xn == (x,) = E Xo Xc] is obtained from a single denoising step using Tweedie's 's formula: [[/xo|x,] = of V (x.)
This formulation has been successfully used to guide diffusion models in solving a variety of inverse problems across scientific domains Zheng et al. (2025). CoGuide builds on this framework but makes a necessary departure owing to the challenges posed by the path-planning operator. We briefly discuss these operators before formulating the floorplan inference inverse problem.
Path Planners as Forward Operators. Unlike typical inverse problems (e.g., deblurring or inpainting),
our measurements y are human-walkee trajectories: thus, the forward operator AA encodes walking policy through an indoor layout (the floorplan x). Directly modeling human navigation is difficult, so we use path-planners as proxies La Valle (2006); |Hart et al. (1968); Gammell et al. (2013); [ Noreen et al. (2016). Empirical evidence indicates people favor short, direct routes with few turns Tong & Bode (2022) which aligns well with shortest-path planning. Therefore, the classical A algorithm Hart et al. (1968) is a good choice for the forward operator. The floorplan is discretized into grid graph, and given a start and end location, A# computes the shortest collision-free path, which we take as the predicted walking trajectory. To fit into the inverse problem framework, differentiable variants of A* such as Neural A Yonetani et al.) (0021). TrahsPath Kirilenko et al. (2023), NRRT Wang et al. (2020) and Takahashi et al. (2019) are of interest; they all aim enable gradient-based learning. Recent diffusion-based planners (DiPPeR Liu ce al. (2024). PbDiff Luo et al. )2024) further provide differentiable path generation. When inserted as forward operators, these planners often induce non-smooth objectives where small layout perturbations can
Luo
cause large path changes, which in turn hinders convergence in gradient- optimization. We analyze causes for this instability next and motivate our embedding-spee space likelihood surrogate.
3 METHOD
3.1 PROBLEM FORMULATION
Fig. 1 (Left) top row shows an unknown 2D floorplan x E ER A user walks in this floorplan (from point A to destination point B. then from B to another destination C, and so on) and records a sequence of location measurements, The union of all these location measurements on 2D image gives us the trajectories y E Raxa as shown in Fig. (Left) bottom row. Given location sensors are noisy, the forward process is A((x) + where A: TR me + Rmxx approximates the human walk using (generally nonlinear) A # path-planner, and n models additive Gaussian noise of the location sensor.
Our goal is to utilize the DPS framework (Eq. 5 below) for which we need the likelihood score.
Vx, xx log.o (()ly))))))))) (xx,)) Vx, logpe (8o)
(5)
Approximating the likelihood requires propagating &e through the A(.) operator and using its gradient to steer the diffusion prior so (x),;; doing this stably lies at the heart of our problem.
The stability issues are due number of factors, partly depending on the realization of the A(-) operator. Observe that a path planning algorithm must perform local searches at every intermediate point while growing a path from the source the destination. The path grows new pixel when that pixel index minimizes the path cost towards the destination; this argmin operation makes the process non-differentiabll Differentiable approximations such as Neural A* (NA*) Yonetani al. (20TT) Transpath Kirilenko et al. (2023). and DiPPeR Liu et al. (2024), mitigate the pixel selection problem. however, the Jacobian derived from the likelihood score proves to be very sensitive. Said differently, the likelihood score Vxlly- -x 2 (x)x(xxx(X) Ty (x)) contains the Jacobian (x(x) and [|)(()]] is large. Intuitively, this happens because the path chosen by the planner immune to most pixels in the floorplan; however, if few pixels change slightly, then the new chosen path can be dramatically different.
To visualize this effect, the top row of Fig. (Left) shows paths from different path planners on a floorplan with fixed start and end locations (note that N outputs all the visited pixels called histories to remain differentiable). The bottom row is a slightly different floorplan where three small doors have been introduced in each of the lower vertical walls. These door pixels prompted the planners to significantly change their paths, indicating a highly non-smooth A(-). Steering the diffusion prior sp (x,,t) with such non-smooth guidance from the Vx, log.. (y/s)) (x)) is unstable. Lastly, these planners generally trained
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A
Neural A*
Transpath
DiPPeR
8
S
Rorplans
(Left) Example floorplan and measured human-walked trajectory (Right) Paths chosen by A Neural A TransPath, and DiPPeR from the start and end locations ("s" and "e"). The bottom is slight change from the top row, prompting a large change path selection.
Figure 1:
on binary floorplans (black walls and white empty space) and must cope with continuous-valued inputs (gray pixels) during the reverse diffusion process.
3.2 GUIDANCE THROUGH CONTRASTIVE LEARNING
Given the complexity in harnessing differentiable path-planning operators, we side-step the issue entirely, Instead, we propose design surrogate for the likelihood score in a learned embedding space C Rd that is smooth (Lipschitz, without discontinuities) and aligned (compatible floorplan-traectoor pairs map nearby, mismatched pairs far apart). We expect that operating in such a space will stabilize the gradients of the likelihood surrogate, making the guidance to the denoiser smoother. Of course, we need to ensure that this new likelihood score from the embedding space still a valid approximation for the original likelihood score Vx, logp: (y/ (xn).
We construct the space E using two encoders fop (for floorplans), and Po (for trajectories):
(6) where x A' and y such that (x) 1 (y)ll2y11) We then use these encoders to define the -oiiiiiod----eeieeeeee ratio for pair of inputs (x.y) in the form of an un-normalized distribution m.
f E' x+ [x|e fx)) Iv: Y-E, y ->> (y)
π (y.xx (x)
where, (...) denotes an inner product, and temperature 7 controls the concentration this distribution on the unit-hypersphere in Rd. Intuitively, when this embedding space is leamed correctly, larger inner products should correspond to higher pairwise compatibility and thus higher likelihood.
To achieve this, train and De contrastively using InfoNCE-style loss function KhaK al. (2020); (ordd et al.] (2018). This approach naturally organizes the embedding space by pulling matched pairs together while pushing unmatched pairs apart Wang & Isola (2020).
Contrastive similarity as a likelihood surrogate. To see why this a valid approach to approximating the true likelihood, note that InfoNCE links contrastive learning density estimation. Following Oord et al. (2018) we observe that the optimal contrastive classifier recovers lkkliioodd-t--evideee ratio. That is, when the InfoNCE loss attains optimum, we get
exp((f p(yix) 1
(f. logp(y logp(y))
(8)
ply)
where C is constant independent of x. Taking gradients with respect to x in Eq, exactly recovers the likelihood score on the right-hand side i.e., _V, (f (x).((())).)())))) = vxlogr(>>x) Therefore, we substitute this surrogate likelihood into Eq. 5) along with the DPS approximation which gives,
1 Vx. (f((x0(x))) T
V log (x:l)) 80 (x. ()
(x(!))
Vx,
2r
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The second equality is valid since unit- norm embeddings satisfy (u.))=1-/luvv) Therefore, the contrastive guidance admits an equivalent squared-distamm form. On the whole, since f.) and gp(+) are smooth, the resulting gradients of (xxx)x)()))) 96 (y) are stable, steadily steering the reverse diffusion toward floorplans whose embeddings compatible with the measured trajectory. We extend this connection from InfoNCE to supervised, multi-positive contrastive learning Khosla et al. (2020); this is natural in setting since several compatible trajectories be synthesized from a single floorplan. This modification maintains the validity of the likelihood surrogate during inference as explained Appendix A
Contrastive Loss Functions. We train the encoders fa and Ie with symmetric supervised contrastive objective. In this setting, let p (x.y) denote the distribution of matched (positive) floorplan-trajcctory trajectory pairs, and p(x), p(y) be the marginals used to draw negatives. The expectations below are estimated with in-batch negatives (and multi multi-positives when available).
Iy exp ( (y))/T)
--=1- log
(10)
(xp((f((x)) (v)))))))(x)))))))) + (exp))))((pppppppp
})/+)]
Here the floorplan x acts the anchor, and the objective pulls the matching trajectory y close to (x) while pushing away non-matching trajectories y ~ p(y). This aligns trajectories around the correct floorplan anchor and shapes a locally smooth neighborhood in E.
Conversely, we anchor on the trajectory y and attract the matching floorplan x while repelling non-matching floorplans x p(x). This complements the floorplan-anchored view and provides bi-directional consistency of the embedding space
L -Exc))--- log exp (x)(( + (exppp
(11)
Adding Alignment Losses, To improve performance, we train the contrastive model with the sym- metric losses in Eq. 10 II along with an additional alignment loss that further pulls each matched floorplanttrrecctory pair closer in the embedding space.
Lalign
E (xyiipp' (y) (x)
(12)
While the loss Leentr separates positives from in-batch negatives, the alignment term primarily tightens each true pair by shrinking the intra-positive L2 distance. To avoid hindering learnngg in early epochs, start with only - and Li-y and slowly increase the alignment weight after few epochs. This schedule gives cleaner clusters in the embedding space and better results in practice. CoGuide's 's final contrastive loss function now be expressed as:
Contrastive Space
0
0
00
Leorm = +
Datign Calign
:
where Ae [0,1] and Aatign > O are hyperparameters.
*y. t7o
Fig. 2 visualizes the t-SNE plot of the leamed embed- dings. Although the figure and legends are dense, they shed valuable light on how the E space is organized. The plot is for different floorplans and trajectories, but let's focus only on the bottom left corner, the region with circles. Two observations are of interest: (1) The green solid circle a specific floorplan x and the orange and blue solid circles are slight variants of x, denoted x & and x Observe these variations are nearby while other floorplans (solid stars and solid squares) are far away. (2) The green circles are trajectories from the green floorplan (the same is true for other colors), and a larger radius indicates denser trajectories. Observe that trajectories are embedded near their matching floorplan, and sparser trajectories are further away from the floorplan (towards the center). This is expected because sparse trajectories imply more ill- posed behavior since many other floorplans can also explain those trajectories. We expect this organization generate smoother likelihood scores, serving the original purpose of CoGuide.
ill-posed
Figure 2: t-SNE embeddings from CoGuide for floorplans (solid green) and two perturbed variants (solid orange/blue). Trajectories from these floorplans are shown as hollow shapes; larger hollow markers indicate higher trajectory density.
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Algorithm CoGuide: Contrastive Diffusion Guidance for Spatial Inverse Problems
Require: timesteps; trajectory y; step sizes {Gr}; Adam base LR 1oo 71, E, noise scales {āt}: diffusion params ((r,ūte) score sp: encoders fes temperature 7, intersection weight Aine-
Require: Annealing & gating: start la. end l. min-LR stop /stpp
1: XT (0,T); Adam: m++0, 0, 0+-
2: for port=--11 down to 0 do
DDIM: 3 80 f); 80 5. 1/2
score model and one step denoising
ēt X - Vo,00
3:
z z~N(0,1). 02 a2t
x 5- V8- v
C,_1 a of +.
b ddim step
4: CoGuide: Gt
Xx
l (y) for (Rt))l 1 Aime Vx IIy@(1 -
contrastive likelihood
2r
Adam: 1/. 4- AunealLll(pppppttea... if Loop set and 12 Luup then < O. <- Adain GG 7a 62. (Optional SGD: x < x
5:
6: return Xo
3.3 IMPROVING DIFFUSION INFERENCE
Intersection Penalty. We found it helpful to penalize intersections between walls and trajectories during inference. We add intersection penalty Cimensect = y (1-xo)l1 all baselines and CoGuide, which counts (up to a constant scale) the total number of pixels where trajectory overlaps wall. Minimizing this penalty term during reverse diffusion steers updates toward wral-trajectory compatibility, yielding floorplans that respect the observed trajectories y.
Using Adam with DDIM. Once the likelihood surrogate and intersection penalty are plugged in, CoGuide performs gradient-based optimization over nonconvex posterior via DDIM [Song et al. (2020a) DDPM Ho et al. (2020). Since DDIM uses fewer reverse steps than DDPM, plain GD/SGD can under-integrate our embedddingbassee gradients, leading to poor convergence. We therefore replace GD/SGD inside each DDIM step with Adam Kingma& Ba (017)). This supplements the reverse diffusion process with higher-order information about the optimization landscape and improves convergence. Algorithm SrrnthhmmTreeee reflects this change by using Adam in the guidance step. To control guidance strength over the short DDIM schedule, we use a brief cosine annealing (denoted as AnnealLR in Algorithm [ of the learning rate. Before the ramp starts (for & t.) we keep the rate fixed at 7)t 1)0 after the ramp ends (for t2 tz) we clamp it to Thiin = pmp. During the ramp (t, <t<te) we use:
Mmmin (no Mmin)) + [
(13)
Finally, we hard-gate guidance off by setting r = for Istp This pairing, Adam for robust, per- coordinate integration and short cosine ramp with a hard stop, recovers much of the "many-step" integration that DDPM would provide while preserving DDIM's speed. addition, it avoids late-stage instabilities by letting the diffusion prior refine the sample without additional guidance.
Using conditional diffusion priors. (CFG+CoGuide) Using conditional diffusion priors, we further boost performance by combining CFG-trained diffusion model sy (x,,tt,c) with CoGuide they can complement each other's 's strengths. In this setup, CFG acts as a conditional prior over floorplans given trajectory measurements c; the only change is that replace CoGuide's original diffusion prior with the CFG model. When the null input c= 2, we recover the original CoGuide behavior.
4 EXPERIMENTS AND EVALUATION
Blind Inverse Problems. CoGuide extends naturally to blind inverse problems. We demonstrate this in Appendix D through restoration of historical audio recordings containing fully unknown degradations.
Datasets. We use the HouseExpo dataset Tingguang et al. (2019) for all experiments. It contains approx- imately 35,126 2D floorplans of houses and apartments generated from the SUNCG dataset (Sogg et al. (2017). We downsample each floorplan to a 64x 64 binary image where white pixels represent free space and black pixels represent walls/obstacles. Downsampling necessary because path-planners scale poorly

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to higher image sizes making diffusion inference slow. We split the dataset into an 80-10-10 for training, validation, and testing. Next, we generate compatible trajectories on each floorplan using the A algorithm by randomly sampling start and goal locations from the open spaces. In addition, at three levels of densities; sparse, moderate and dense to simulate how much a user may have walked. On average, these correspond 40%, 25%, and 10% of the open spaces in the floorplans, respectively. Including this in the dataset helps in understanding how CoGuide copes with ill-posedness as the number of measured trajectories decreases.
generate trajectories traaeeetto
Metrics. We evaluate the performance of different methods using 2 metrics: (A) Intersection over Union (IoU) Rezatofighi et al. (2019): This metric measures how well the predicted floorplan overlaps with the ground truth. It is computed as the ratio of intersecting free space pixels to the union of all free space pixels: IoU [PPEEP|
[PUPP" where FP and FP" are the predicted and true free space pixels, respectively. (B) F1 score
Sokolova & Lapalme (2009): Defined as F1 = 2xPxR where P is the precision and R is the recall of the P+R
bitmap. P and R are detined based on free space pixels, similar to IoU.
Baselines. We evaluate the performance of CoGuide against 6 competitive baselines: DPS+X: DPS Chung et al. (2022) based inverse solver with 3 differentiable path-planners AA by instantiating X E (NeuralA* * rransPath, DiPPeR) Yonetani et al. (2021), Kirilenko et al. (202)), Liu et al. (2024). More details about the planners are provided in the Appendix [E] Briefly, these path-planners use CNN- based shattest-path module, a Transformer based path- probabiiityy encoder, and a difffssinn-base planner, respectively. DifIPIR Zhu et al. (2023); A plug-and-play image restoration solver that uses a diffusion model as a denoiser, instead of training a Gaussian denoiser. We follow the default configuration given in their publicly available codebase. Since do not have closed form data proximal estimator, an ADAM optimizer to solve the proximal with 10 optimization steps and use a 0.01 learning rate. DMPlug Wang et al. (2024) A recent inverse solver that optimizes the noise seed such that after DDIM sampler, the resulting image satisfies the measurement constraint. We follow the default configurations in their publicly available codebase except use 100 optimization steps. CFG: Performs Classifier-free Guidance Ho & Salimans (2022) that combines the unconditional and trsjectory-conditionedd scores with a guidance scale that biases sampling toward majectory-consister floorplans.
Real-world Evaluation. To evaluate real-world performance, we collected trajectories in two student apartments using the Qorvo DWM3000 ultrawideband (UWB) module Qorvo (202I). The UWB system consists of multiple anchors statically placed at known 3D coordinates in the apartment. The user carries a beacon that periodically pings each anchor, allowing us to estimate the time-of-flight (ToF) to that anchor. Using the ToF to line-of-sight anchors and the known speed of light, we apply trilateration to estimate the user's 3D position. We then project the 3D position onto the 2D floorplan to obtain walking trajectories. The UWB setup provides locations with standard deviation of 10 cm. It is worth noting that the trajectories obtained via this method are sparser and distributed somewhat differently compared to our A *. generated dataset.
Diffusion and Contrastive Model. Our base diffusion model follows the implementation used in the DPS Chung et al. (2022). We adapted the contrastive model used in Khosla et al. (2020) based on the specific needs of this project. Classifier-free guidance (CFG) from the diffusers library is implemented by training single UNet denoiser that can operate in both conditional and unconditional modes, and then combining its two outputs at inference with a guidance scale. The underlying idea and architecture come from the original classifier-free guidance paper Ho & Salimans (2022). We discuss architecture and hyperparameter settings for all our models in Appendix
4.1 RESULTS
Quantitative. Table I reports F1/loU (mean + std) for three regimes of trajectory density: sparse, moderate and dense. the sparse regime, CoGuide attains the best performance, exceeding all baselines, including CFG. Similarly in the moderate regime, CoGuide again leads surpassing CFG and the DPS variants. However, in the dense case, CFG is the strongest, with CoGuide showing comparable performance. Overall, CFG+CoGuide consistently outperforms CFG, D DiffPIR, and DMPlug.
Qualitative. Fig. S shows qualitative results of CoGuide and baselines on 6 test floorplans. The ground truth floorplan shown in the top row with the measured trajectory y marked in light blue. The DPS+planner-burt based methods along with DifiPIR and DMPlug fail to produce valid floorplans that are consistent with the provided trajectory, often generating artifacts on the converged floorplan. This can be
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Ground Truth
DPS Neural A
DPS + Transpath
DPS DiPPeR
DMPlug
DiAPIR
CFG
CoGuide (Ours)
Figure 3: Qualitative comparison of ground truth floorplans against baselines and CoGuide.
attributed the unstable forward operators A(.) that get embedded in each of their optimization processes. It is worth noting that although CFG proves superior in metrics, qualitative results do not always reflect this fact. CoGuide shows floorplans that are consistent with the trajectory, with fewer visual artifacts, We show additional results on different floorplans in Appendix B
4.2 ABLATIONS
Effect of the Intersection Penalty. As described in section 3.3 the posterior sampling to improve our results. Table 2 shows the effect of this intersection penalty on the results. We observe two points: (i) Inclusion of the intersection penalty Lintersect leads to improvement the evaluation metrics and the qualitative results. (ii) Applying too large of penalty, however, degrades the results, be seen in the last row when Xan 1.5 10-3
we add an intersection penalty to guide

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Table 1: Performance across trajectory densities. Each regime reports Fl and IoU (higher better).
Sparse
Moderate
Dense
Method
F1
IoU
F1
IoU
F1
IoU
NPS+Neural
0.09
0.67=0.13
0.79++0.09
0.66+0013
0.79++0.09
0.66++0.12
DPS+TransPath
0.76++.15
0.64+0.18
0.74
0.60+0.19
0.72+0.17
0.59+0.200
DPS+DiPPeR
0.77
0.6410.13
0.7710.11
0.6410011
0.76100.11
0.63
DMPlug
0.31110.10
0.19
0.28.10.09
0.17
0.28.10.08
0.16.10.07
DimPIR
0.63:10.09
0.47
0.64
0.48
0.65.10.08
0.49
CFG
0.86
0.76=0.10
0.93++003
0.88+0.05
0.97
0.93
CoGuide
0.01
0.84
0.94
0.89+0.05
0.95
0,90
CFG+CoGuide
0.93+0.04
0.87=0.07
0.97=0.03
0.93=0004
0.99
0.97
Improving convergence with Adam
As discussed in section[3.]] we incorporate the Adam update during the sampling process and compare against the standard Gradient descent-based update. Results reported in Table show that employing Adam consistently outperforms SGD across both DDPM and DDIM.
Measurement Noise. In real-world settings, the localization sensors that provide the input trajectories to CoGuide may be noisy. To model this, we inject Gaussian noise into the trajectory generation process. We increase the noise standard deviation and compare our performance across various noise levels and trajectory densities. CoGuide's performance understandably degrades with increasing noise levels as shown Fig. The degradation is, however, graceful, and denser trajectory upholds better performance.
Table 2: CoGuide performance across intersection weight Ana settings.
Sparse
Moderate
Dense
Aint
IoU
F1
IoU
IoU
0.0
0.88=0.05
0.78
0.90
0.82
0.91 :0.04
0.84
3.0 10
0.91=00044
0.83+0.07
0.93
0.88+
0.95+0008
0.90
7.0 10
0.91
0.84
0.94
0.89
0.05
0.90
1,5 10
0.91=0.04
0.84
0.93
0.88
0.94
0.89
Table 3: F1 and IoU for optimizers under DDPM and DDIM samplers.
0.9
Dense
Modorate
Sparso
0.8
0.7
0.6
Sampler Method
F1
IoU
0.5
GD
0.92+00.4
0.87-0.05 0..7
DDPM Adam
0.94 0.88
0.3
0.00
0.25
0.50
1.00
1.50
2.00
2.50
DDIM
GD
0.86+0.07 0.76-0.10
Noise std. dev. 6
Adam
0.92
0.85
Figure 4: Effect of measurement noise on IoU.
4.3
REAL-WORLD FLOORPLANS
We compare CoGuide and CFG on real-world floorplans using UWB measured trajectories. Although both methods make errors, CoGuide substantially infers more wall segments and room structure, whereas CFG often misses or misplaces major walls (Fig. 5). This gap is expected since CFG explicitly trained to model the posterior density under the synthetic A ---gneratedd trajectory distribution, and therefore struggles to generalize when faced with the sparser, differently distributed real-world trajectories.
Additional inference results more floorplans and uncertainty analysis are provided in Appendix B Finally, Appendix we also demonstrate generalization of CoGuide beyond spatial inverse problems by tackling blind audio enhancement application. Audio demos provided on website.
5 RELATED WORK
Floorplan estimation has been studied extensively for a range of applications including room/graph reconstruction, layout parsing, and indoor mapping Lee al. (2017); Gillsjö et al. (2023): Yang et al.

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Apartment
Human Walking
Trajectories
CFG
CoGuide
Figure 5: Real- results comparing CFG and CoGuide apartment layouts. CoGuide recon-
structed floorplans are qualitatively superior to those of CFG, which shows spurious structures.
(202)) Zou et al. 2018). Early works include classical and unsupervised pipelines leveraging mobile sensing and heuristics Shin et al. (201T). Graph-based methods have also been explored Yang et al. (0233
Hickman Krolik (2009), modeling spatial relations via polygons, and wireframes.
Vision-based approaches using RGB images remain common and highly effective Lee et al. (017): Zou
(ct al. 2018); Zeng et al. (2019); Lv et al. (202I); y Yan al. 22020); JJa et al. (2022). large body of work reconstructs floorplans from RGB images panoramas via comee/edge decoding or Manhattan layouts Lee et al. (017);; ; Zou et al. (0188: Lv et al. [2021); Zeng et al. (2019), with extensions to 3D room layout from single view Yan et (2020): Dia et al. (2022). More recently, diffusion models have emerged as strong priors for layout synthesis and reconstruction, including constrained or vectorized floorplan generation Gueze al.
(2023): (Shabani et al. (2023) Inoue et al. (2023), leveraging advances in score-based modeling Ho et al. (2020); Song et al. (2020a); Rombach et al. (2022). While these visual pipelines are effective when imagery is available, they raise privacy concerns and depend on line-of-sight
and scene illumination. In contrast, trajectories are privacy-preserving. and can be casily collected during routine motion using built-in IMU sensors on mobile devices.
Other modalities have also been explored beyond vision. including acoustics Zhou et al. 201 magnetics Luo al. (2017). RF Peng al. (2018); Ammalla al. (2025), radar HHikkman & Krolik (2009). While effective in specific settings (e.g.. BatMapper /hhou et al. (2017)) many require specialized hardware or calibrated infrastructure, whereas IMUs-ased trajectories are easy to obtain. Walk2mapl Mura et al. (202T) is the most relevant
work, but it is designed only for single-room floorplan layouts and does not have any underlying generative capabilities.
CONCLUSION AND FOLLOW-ON WORK
This paper focused specific floorplan estimation problem, however, the notion of contrastive guidance should lend itself to a broader family of non-differentiable operators. We intend to investigate what family of operators can benefit, and conversely, how the contrastive guidance be improved to broaden that family of operators. Along these lines, newer applications are also interest. For instance, can city maps be synthesized based GPS trajectories of vehicles? Can discrete molecular structure be synthesized from measured properties molecules? Can Internet topologies be derived based on streaming packet analyties? Towards Realism: Even in our specific floorplan application, there is room for improvement. The standard floorplan dataset (HouseExpo) does not include furniture. Incorporating furniture into the environment
Spatial Inverse Problems:
practical extension, if such dataset is available. Blind Inverse Problems: This paper showed early results solving blind inverse problems in the audio domain. We believe this offers promise that contrastive guidance can enable a line of attack for complicated blind inverse problems, long we can generate measurement, from known x's. If partial information is available about A(.), could that be adequate to design a good embedding space? We believe CoGuide could initiate conversation along all these branches, inviting range of follow-on research and exploration.
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ACKNOWLEDGMENTS
We are grateful to Sahil Bhandary Karnoor for helping us collect real-world data. We thank the anonymous reviewers for their valuable feedback. This work was partially supported NSF #2008338, #1909568, #2148583, and #MRI-2018966. This work used DELTA at NCSA through allocation CIS230230 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services Support (ACCESS) program, which supported by U.S. National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296
7 REPRODUCIBILITY STATEMENT
All model implementations, training scripts, and inference pipelines are open-sourced and will be made available in our anonymous GitHub repository. This includes instructions for environment setup, depen- dencies, and reproducible random seeds. The datasets used in experiments publicly available. We provide detailed descriptions of the dataset in Sec. f. Hyperparameters, training schedules, and evaluation pipelines are described in Sec. 4 with further details in the Appendix [ code repository can be found at httttttss//couuiiiii github. io/
Additional information including
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APPENDIX
1. Theoretical Notes
(a) Likelihood under InfoNCE objective
2. Additional Qualitative Results
(a) Additional floorplans
(b) Sensitivity to trajectory sparsity
(c) Top-5 Nearest Floorplans in the Contrastive Space
3. CoGuide Implementation Details
(a) Diffusion Model Training
(b) CFG Model Training
(c) Contrastive Model Training
(d) Supervised Contrastive Loss functions
4. Generalizing CoGuide to Blind Inverse Problems
(a) Blind Audio Restoration
(b) Method
(c) Contrastive Model Training and Implementation
(d) Results
5. Details on Path Planners used in this paper
(a) A#
(b) Neural A#
(c) Transpath
(d) DiPPeR
A LIKELIHOOD UNDER INFONCE OBJECTIVE
CoGuide benefits from the replacement of the likelihood term with contrastive similarity score from the InfoNCE formulation. This relationship was originally shown in Contrastive Predictive Coding Cord cet al. (2018). We include this derivation here for completeness the context of CoGuide. Consider batch of size N with floorplans X'= { and trajectories y'=
{y y For given floorplan xj, we shuffle the trajectories 5i, so that the corresponding positive trajectory y, placed at position iE NJ. The task is then to identify the correct index i. Let be the random variable representing the index of the correct trajectory for floorplan X, and p(y) be the marginal distribution trajectories. Given similarity general score function s(x.y), the InfoNCE assumes a softmax distribution over indices and formulates cross-entropy loss. So, the approximate posterior q:
exp(s(x,,11)
(1=1|xx,)) E exp(s(x),ys)
(14)
Specifically, in CoGuide, the similarity score function assumes the form s(x,y) (x). (y))) since encoders fop (for floorplans), and gy (for trajectories) descrihed in section 3.2
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Next, calculate the true optimal posterior p from Bayes' rule as:
p(x,,i1==1) N
[1==i|,]> 3)
p(xx, (-r)
ply: (x,) Leys)(yss)
r= p(y+ (21g (RATT(A)
ply: xxi]] (IsAi)(())))
En ply, (xy) [1)
p(ys|2,)
ply:)
- p(yr x2)
p(yr)
Matching q and requires that the numerators differ only by a multiplicative constant (since softmax is invariant to shifts). Thus, at the InfoNCE optimum.
(xp(((((xx) ply x))
p(y)
1 (x) (x)) x logr(y| xx) - logp(y)
(x)))) (y)) = logp(y logg(y(()++
(15)
where C' is independent of x. Taking gradients with respect to cancels both logp(y) and C, yielding
(a(fp(()) [xlogp((|>x)
(16)
Therefore, we substitute this surrogate likelihood into Eq. 5 at time-t along with the DPS approximation which gives,
Vx, (x.log((xly)) =0
xx
т 1
xx (1 (1-561666 ((o (x.)
g (x)))
(x)(
Vx. (5t -x
(17)
27
We replace the inner product with the Le norm since (x) 11 (v)ll2 This derivation justifies that the InfoNCE objective ties together contrastive similarity with the true likelihood p(y x). On extending this approach supervised contrastive training, the validity of the likelihood surrogate Vx,
remains intact. This holds because, during diffusion inference, only single measurement y is provided to the model. Thus, the contrastive objective in this case uses only single-positive sample, and therefore, reduces to that of the InfoNCE objective.
laf (xx(x)) Py (y))l
B ADDITIONAL QUALITATIVE RESULTS
Additional Results. show results from evaluation on additional floorplans in Fig, Gand We observe that CoGuide significantly outperforms all inverse-solver buselines while being better or comparable to CFG.
Uncertainty quantification. We eventually imagine a user-in-the-loop system because not all floorplans observed with the same number of trajectories; estimates vary and should reflect this effect. We therefore quantify an uncertainty in the predicted floorplan by drawing multiple posterior samples and computing the variance of its distance transform SStutzz (2023) while allowing for small translations.
Fig. 9 shows the uncertainty in the predictions of CoGuide across varying trajectory densities for two different floorplans. As is expected, increasing trajectories lead to reduction in the uncertainty, as
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Ground Truth
Neural A
DPS Transpath
DPS DiPPeR
HOTL COLLE
DMPlug
DiMPIR
CFG
CoGuide
Figure 6: Qualitative comparison of ground truth floorplans against baselines.
highlighted by low amount of red towards the bottom right of the images. In regions of high uncertainty (more red), a user practical-setting may collect more trajectory measurements those regions. the measurements increase, the uncertainty in the predictions decreases eventually converging to the true floorplan.
Top 5-nearest floorplans in the contrastive embedding space We include figures to show the closest floorplans to the ground truth in the contrastive embedding space. It is evident that the contrastive space has learned place semantically similar looking floorplans close-by. This further supports the original goal CoGuide. We observe that as sparsity increases, the top-5 retrieved floorplans move away from the ground truth. This reflects the nature of the contrastive space that shown in the t-SNE plot in Fig.
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Ground Truth
DPS Neural A+
DPS + Transpath
DPS
DMPlig
DiMPIR
CFG
CoGuide
Figure 7: Additional qualitative comparison of ground truth floorplans against basclines.
C
CoGUIDE IMPLEMENTATION DETAILS
C.I
DIFFUSION MODEL
Inputs and outputs. All models operate on single-channel floorplan images of spatial size 64 64. The network predicts single-channel output and is trained estimate the diffusion noise (i.e., parameterization). Training was done on NVIDIA RTX3090 GPU with 24GB RAM for around 28 hours.
Backbone topology. We use a U-shaped encoder- decoder with skip connections at every resolution. The base feature width at the first stage is 128 channels. The network has four resolution stages with channel
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Figure 8: Variance Distance Transform across 5 random seeds to quantify uncertainty. Decreasing uncertainty with increasing trajectory density marked is evident from the reduction in red regions.
multipliers (1,2,3,4) applied the base width spatial resolution decreases, yielding encoder widths 228,256,384,512. and a symmetric decoder. Each resolution stage contains one residual block per scale. Residual blocks follow the sequence: normalization layer, SiLU nonlinearity, x convolution, followed by a second normalization-Sile Siriiioon---LUU-- SiLU-3 x stack inside the block; a skip projection is included when input and output widths differ. Spatial downsampling and upsampling are performed inside residual blocks (residual down/up blocks), keeping the skip topology consistent across scales.
Timestep conditioning. Diffusion timesteps are embedded with sinusoidal features and passed through two-layer multilayer perceptron with SiLU activation. The embedding dimensionality equals four times the base feature width (4x 128). This vector conditions every residual block through feature-wise affine modulation (scale and shift applied after normalization). When scale shift modulation is disabled, the embedding is added to the block features instead.
Self-attention. Self-attention is inserted at the stage whose spatial size equals 64/16 64/16=4 (i.., after fourfold downsampling). Attention uses four heads with 64 channels per head. Query, key, and value projections computed with convolutions over the flattened spatial axis; the dot-product weights scaled by
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Ground Truth
Top 5
Dense
Sparse
Figure 9: Retrieving floorplans corresponding to the top nearest embeddings to the ground truth floorplan. The top-1 is always the ground truth itself.
1/Vd for stability, and the attended features are projected back to the model width with a x convolution. The same attention configuration mimored on the corresponding decoder stage.
Normalization, activation, and regularization. All blocks use the same normalization layer before SiLU activations. Dropout is disabled (rate =0) throughout the network. Zemo-initialized x convolutions used at the end of residual blocks to stabilize early training.
Output head. After the final decoder block, a normalization and SiLU applied, followed by a 3 convolution that maps the current feature width back to one channel. No auxiliary heads are used.
Encoder-only and super-resolution variants. For representation studies, that shares the same residual and attention layout, terminating in either adaptive average pooling or attention pooling to produce compact vector embedding. For conditional super-resolution ablations, a second (low-resolution) image is bilinearly upsampled to the target size and concatenated with the noisy input along the channel dimension before entering the first convolution; the rest of the architecture unchanged.
encoder-only variant
Optimization and schedule. Unless otherwise noted, we train for a long horizon (on the order of 10 epochs) using AdamW with weight decay 0.05, an initial learning rate 10 10-4, gradient- norm clipping at 1.0, and cosine decay to zero floor following short warmup of 1-2% of total steps. Exponential moving averaging of weights (decay 0.999-0.99%0 0.9999) can be enabled for evaluation stability; results are reported with and without EMA when relevant. Batch size is chosen to saturate device memory; when necessary. gradient accumulation used to reach an effective batch size comparable across setups. Dropout within residual blocks is disabled (rate O).
Diffusion hyperparameters. We use linear variance schedule with --=100 steps. Training targets are the additive noise (epsilon- parameterization). The predicted mean follows the standard epsilon formulation, and the variance handled inside the diffusion objective with a learned rannrre parameterization; no auxiliary output heads are added. Denoised clipping is enabled, dynamic thresholding is disabled, and
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timestep rescaling is not used. For sampling, report both ancestral sampling using the full 1000 step trajectory and deterministic sampling using 100-step respaced trajectory.
Tunable knobs. Architectural knobs include: base width (default 128), the four-stage multiplier tuple (1,,,3,4), number residual blocks per stage (default one), attention placement (default only at 4 x 4), number of attention heads (default four), and head width (default 64). Training knobs include: total epochs (order of 10²), optimizer (AdamW), weight decay (10-2-10- default 0.05), initial learning rate x 10-3 x 10-4, default 10-4), warmup ratio (1-2%), cosine decay floor (zero), gradient-norn clip (default 1.0), EMA decay (0.999-0.9999) and batch size or effective batch size via accumulation. Diffusion process knobs include: number of training steps T (default 1000), schedule type (linear by default), respacing for sampling (full vs. 100-step DDIM).
C.2 CLASSIFIER- GUIDANCE
Architecture. Our diffusion prior uses U-Net backbone with four resolution stages. The network operates on 64 x 64 inputs and increases its channel capacity across levels (128,256, 256,384,512) Each resolution stage contains a single residual block, and standard mid-block is placed between the downsampling and upsampling paths. The upsampling hierarchy mirrors the downsampling path to preserve multi-scale information. We use group normalization with 32 groups throughout the network, and time embeddings are injected via scale-shift modulation in each residual block so that the denoising behavior is explicitly conditioned on the diffuusion timestep.
Training, We train the model for up 50 epochs on 64 64 images with batch size of 128. The learning rate is set to 10 4 with 500 warmup steps and no gradient accumulation. Classifier free guidance Ho & Salimans 2022) is enabled by randomly dropping the conditioning with probability 0.1 during training, so the network learns both conditional and unconditional denoising behavior in a single model. Training is stopped at 50 epochs because the validation curve plateaus.
Inference and guidance tuning, At inference time, we run a standard diffusion sampler starting from Gaussian noise and iteratively denoising toward clean floorplan. Given unconditional and conditional denoiser outputs, su (,) and sp (,)x,,,,)) we form the classifier-free guided prediction
5x, = (x, -x,,(x,,
(18)
where y is the guidance scale. We tune y by sweeping over several values and selecting the setting ? 1.5 that achieves the best validation performance (Fig, 10
Evaluation Metrics vs Guidance Scale
RMSE
Error Value
0.3
0.2
0.1
1.0
2.0
3.0
6.0
7.5
6.0
9.0
10.0
Guidance Scale
Figure 10: Tuning the guidance for inference in CFG
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C.3 SUPERVISED CONTRASTIVE LEARNING
Encoders and embeddings. Both modalities (floorplan and trajectory) processed by identical vision transformers operating at x4 64 resolution with 16x 16 patches. The patch stem is adapted to one input channel. The transfommer produces global token which mapped 256-dimensional space by lightweight two-layer projection: linear - GELU + linear within 256 dimensions, followed by dropout (p= 0.1), a residual connection from the first linear output, and LayerNorm. The resulting vectors are fz-normalized prior to similarity computation.
Batch organization and positives. Mini-batches of size 496 are constructed so that each floorplan anchor is paired with 7 trajectory positives from the same scene, yielding nulti-positive setting. All other examples in the batch that do not share the label act as negatives. Similarities are inner products between unit vectors.
Data transformations. To encourage invariance, random rotations and borizontal/vertical flips are applied to both modalities. For trajectories, structured dropout removes uniformly sampled fraction of path pixels in the range [0.05,0.10] per sample. Augmentations are applied independently across views.
Contrastive objective and temperature. The supervised contrastive term temperature scaled log-softmax over all candidates in the batch. The temperature is learned during training, softly constrained to the interval [0.01,0.15). and updated with small learning rate (10-4). Unless stated, the contrastive weight is initially one and may be decayed later (see scheduling).
Cross-modal alignment and directionality. Alongside the symmetric multi-posinn positive objective, add a distance-basee alignment penalty between matched Hoorplan-trajectoryy trajectory embeddings to co-locate cor- responding pairs. A directional coupling that emphasizes pnjeectory-tofloorppe consistency is included with half the weight of the main contrastive term. A uniformity regularizer (energy-based) is available but disabled by default.
Loss scheduling, After an initial phase of 100 epochs, the contrastive weight is optionally reduced linearly over 30 epochs to nonzero tail value (e.g.. 0.5), then held fixed. This schedule prioritizes discrimination early and refinement later. Alternativ schedules that delay the aligmment term use the same ramp length and maintain a reduced contrastive tail; the discrimination first schedule is the default.
Optimization and training hyperparameters. Training runs for 1500 epochs with AdamW, weight decay 0.05, and an initial learning rate of 10 Gradients are clipped to a global norm of 1.0. The learning rate uses a short warmup covering 1.5% of total steps, followed by cosine decay to zero floor. The temperature parameter is optimized jointly under the same schedule.
Optional stochastic floorplan corruption. For ablations, we considered variance preserving diffusion- style corruption applied to floorplan inputs with small probability and limited diffusion time within a 1000-step schedule. This corruption is disabled in the main experiments to isolate the supervised contrastive contribution.
Summary. Single-channel inputs for both modalities; shared transformer encoders at 64 x 64 with 16-pixel patches; a residual two-layer projection to 256 dimensions with unit-length normalization; multi-positive supervised contrastive learning with learned temperature; rotation/flip invariances and mild trajectory dropout; auxiliary alignment and directional terms with modest weights; and a long-horizon AdamW optimization with warmup and cosine decay. Training was done NVIDIA A6000 GPUs with 48GB RAM for around 12 hours.
C.4 LOSS FUNCTION DESCRIPTIONS
Here, we explicitly state the expressions for the contrastive losses that were used in the implementation of CoGuide. The losses include a symmetric supervised contrastive loss along with a positives-only alignment loss. Let (x) be the floorplan encoder and g6 (y) be the trajectory encoder. For each floorplan x,, there K matched trajectories {y Jk=1 Negatives come from other items in the batch.
Floorplan + Trajectory, Cy Anchor: the floorplan embedding Ir (x:). Positives: its matched trajectory embeddings (Y.R). The numerator scores true pair; the denominator sums scores over all
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trajectories in the batch. This pulls in the right trajectories and pushes away the rest.
log
K
B
K
exp (xp((U((xx,)))nxxxx)))
1=I==1
Trajectory - Floorplan, f. Anchor: the trajectory embedding fo (Y.R). The numerator scores its matched floorplan gu (xx); the denominator sums over all floorplans in the batch. This pulls in the right floorplan and pushes away others. Using both directions keeps both encoders balanced.
B
K
exp log
BK
(xp((
Alignment loss, (a).mmeatDDitttt shrinks the distance of each matched pair fx)) (often a= =22). With en-normalized embeddings, this equals increasing cosine similarity.
Calign (a:)
-
BK
Total loss, Liotat- We blend the two contrastive loss terms and the alignment term. In practice, we start with Adign 0 and increase after a few epochs so the contrastive losses first separate negatives, then alignment tightens each true pair.
Cecura + (1-)) + Aalign Calign
Here, xE [0,1] and Aulign >0 are tunable hyperparameters.
D GENERALIZING COGUIDE TO BLIND INVERSE PROBLEMS
CoGuide's contrastive diffusion-guidcae guidance enables to solve blind inverse problems. We apply it to blind audio restoration task on historical piano recordings that are degraded by unknown operators A(-) (including spectral coloration, additive noise and clicks, nonlinear effects, etc.). This setting is fully blind because we neither assume analytical form for A(-) nor estimate it at inference time.
D. FORMULATION.
Like in CoGuide, we formulate this problem with a forward model. Here an unknown clean audio waveform is observed through unknown degradation operator A plus noise o0.
y= -=A(x)+w
(19)
Neither x nor known at test time. The goal recover plausible x that both explains the observation and lies on the manifold of realistic audio samples. Following Karras et al. (2022) and the formulation in Moliner et al. (2024), we parameterize time by a continuous variable т, with x, denoting the audio
sample noisified at time At 7 7= is approximately Gaussian noise, and at = 0, xo corresponds to clean audio. The probability flow ODE for this process given by
dx, = - x, logp (x, )dT,
(20)
where Vx, logp-(xx) is the prior score. We approximate this score using denoiser network so sx(x,,7);
sx(x,.7)
Vx, logp, (x,)
(21)
where 22 is the noise variance at time 7. The network is trained via denoising score matching:
E Exo
(22)
with X(7) chosen as in Karras et al. 2022
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D.2 METHODS
As described in the main text, for inverse problems with observations y, we replace the prior score in equation 20 with the posterior score Vx,logp, (x, (x). Using a Bayesian decomposition Chung et al.
(2022). we split the posterior score at time as
Vx, logp, (x, ly) Vx (x,) Vx,, logp, (y (x,),
(23)
where the first term is the prior score from equation 21 and the second term is likelihood term. In prior work Moliner et al. (2024), the likelihood score is approximated as the gradient of an audio-domain cost function Caudio (y, (x) scaled by normalization factor E(T):
V x, logp: (y xx) -((T)Vx, x, Cy H.l (Xx)),
= Dxx
(24)
where (-) denotes the (known or learned) degradation operator (e.g., frequency-domain equalizer in BABE2). Substituting equation 23 and equation 24 the ODE equation <quation[2jyieldss the posterior-flow:
dx,
V X, logp, (x+ Vx. x, logp, (y xx) dr.
(25)
prior
likelihood
Moliner et al. (2024) uses carefully designed approximation of the A operator and optimizes this quantity while carrying out diffusion inference steps. We sidestep this process via our contrastive framework.
Contrastive guidance. To incorporate CoGuide, we add contrastive guidance term that measures alignment between the reconstruction and the observed degraded audio in an embedding space. We use an encoder, f&p : U' - Red to map candidate clean signal and the corresponding observation into shared latent space. At diffusion time т, we obtain a current reconstruction X = =sp =p((X, and compute embeddings fo (8x) ff (y) We define a contrastive score via the inner product (fo ( with Ez-normalized embeddings to compute cosine similarity.
During sampling, this score acts a surrogate likelihood (like in CoGuide): candidates whose embeddings align with the observation are favored. Concretely, we replace the likelihood term with a contrastive gradient given by:
logpr, (x)x)) Vx., (f (f
(26)
where (()) controls the guidance strength. The resulting CoGuide update becomes
Xr -Xr TT V Xx., logpr, +Tx)) (Fo(())
(27)
where the gradient Vx., is obtained by backpropagating the inner product (x), through both the denoiser sg and the encoder fip-
D.3 TRAINING AND IMPLEMENTATION DETAILS
Contrastive audio model. We train contrastive encoder to embed clean and degraded piano audio into shared latent space. Given a clean waveform x and degraded counterpart y, both sampled at 22,050 Hz and cropped to fixed 8.35 s segments, we first compute their complex Constant-Q Q Transform (CQT)
Velasco et al. (2011).
XCCQ Y- Y-CQT())
(28)
(29)
using 7 octaves and 64 bins per octave. Real and imaginary parts are stacked channels and passed to the encoder fg. This encoder consists of 2D CNN/ResNet backbone over the (I!T') CQT grid, followed by global average pooling and linear projection to a d= 256-dimensional embedding, with Ez-normalization. Training, Clean data are drawn from the MAESTRO dataset Hawthome et al. (2018). and degradations are applied on-the-fly fly in the waveform domain (e.g., low -awssas filtering, spectral coloration. clipping, additive Gaussian noise, and mild dynamic range compression), producing pairs (x,y:). For a mini-batch, we compute similarities by inner products scaled by temperature т. We use symmetric InfoNCE loss so
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Original
LTAS
CoGuide
Beethoven
Chopin-
Chopin-Waltz Waltz
Mozart
Figure 11:
Comparison of spectrograms for the degraded (original), LTAS, and CoGuide across three different pieces of music from the MAESTRO dataset Hawthorne al. (2018)1
that each clean embedding treats its own degraded counterpart as the positive and all others as negatives (and vice versa). For a batch of size B, the loss is
73
Σ B exp (f (X,)) log (Y)
exp (ffo
log
2B
exp (Y. (X.).
The model trained with AdamW (learning rate 10-1, weight decay 0), batch size 256 and fixed temperature r 0.007 until the validation contrastive loss and embedding alignment metrics saturate. The trained encoder f is then frozen and used to provide contrastive guidance during diffusion inference.
D.4 RESULTS
shows qualitative audio samples with spectrograms below for comparison across different com- posers' pieces. CoGuide is able reduce nonlinear effects, perform denoising of nonstationary noise, and enable the reconstruction of high-frequency details. Table Albseent the quantitative metrics using Fréchet Audio Distance (FAD) Kilgour for the degraded recordings, the LTAS baseline, and CoGuide. Our method outperforms the bascline in both metrics. The LTAS baseline Moliner et al. (2024) is an equalization technique that seeks to align the Long-tern term Averaged Spectrum (LTAS) of the audio with reference set. Additional qualitative audio samples are available at oorr project page
Fig. 11
(2018) (computed with VGGNet and PANN Kong et al. (2020a))
Audio Type
VGG J PANN |
Degraded (Original Recording)
2.52
0.39
LTAS Baseline
2.88
0.27
CoGuide restored (Ours)
0.84
0.19
Table 4: Quantitative FAD comparison of CoGuide against the original recording and LTAS bascline for blind audio enhancement. FAD-PANN results are reported after scaling by 10
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E PATH-PLANNING ALGORITHMS
We now briefly discuss the planning algorithms and their implementation details used in our experiments.
E
ASTAR (A*)
AStar is classical path-planning algorithm that computes the shortest path grid maze given start and goal locations. A has been used in robotics and game development for navigation tasks. To compute the shortest path, A maintains two lists: an open list of nodes O be evaluated and closed list of nodes C' already evaluated. At each step, among the open list O A selects the node with the lowest cost (())=g(n)++)a) +h(n), where g(n) the cost from the start node to node n, and h(n) is a heuristic estimate from n to the goal. Once node is selected, it is moved to the closed list, and its neighbors are evaluated (expanded and added to the open list they are not already in the closed list). The process continues until the goal node is reached or the open list is empty (goal not reachable). The heuristic h(n) is typically chosen to be admissible, meaning it never overestimates the true cost to reach the goal. Typically the Euclidean, Manhattan distance Octile distance is chosen as the heuristic h(n). A is complete and optimal, meaning it is guaranteed to find the shortest path if one exists and the heuristic is admissible. In our problem ,we recast A# as forward operator A(.) that takes in floorplan x, start postion and an end position, and outputs the trajectory between the start and goal locations, trajectory A(x,,tart,end) This emulates a human/robot navigation model where the agent always takes the shortest path between two locations. Because A relies on node selection of minimum cost and discrete expansions, it is inherently non-differentiable and hence cannot be used in gradient based optimization.
E.2 NEURAL ASTAR (NA*)
To make the A search differentiable, Neural A* (NA*) Yonetani et al. (2021) was proposed. NA* first encodes the problem instance (floorplan, start, goal) using a convolutional neural network extract guidance map. This guidance map learns to effectively highlight regions of the floorplan that are likely to be part of the optimal path. Then an iteratively differentiable search mechanism is employed on the guidance map to compute the search histories. NA redesigns nodes in the open and closed lists O, C as matrices and replaces the argmin operation with a softmin operation for the backward pass. Along with some clever node expansions and updates, the search process becomes differentiable. Once the goal is reached, a backtracking step is performed to extract the optimal path from the search histories. is important to note that the end-to-end planning of NA # is still non- difeeefferetti because of the backtracking step. So the optimization can be carried only until the "histories" step in the NA algorithm. Finally, note that the only "learnable" component in NA & is the convolutional neural network that generates the guidance map and not the differentiable search process itself. Recall that as shown in Fig [ (Right), small hole opening in a wall can cause large change in the trajectory generated by A * the path can now go through the wall instead of going around it. This behavior would make the histories very sensitive to small changes in the environment, which would make gradient-based optimization unstable.
E.3 TRANSPATH
Transpath Kirilenko et a (2023) is another differentiable path-planning algorithm that is proposed to accelerate the path-planning. By leveraging the instance-dependent structure (for example, floorplan, start, stop), Transpath learns a heuristic function using a transformer architecture Vaswani et al. (2017) to guide the search process. A path probability map (PPM) is generated from the transformer that highlights regions of the floorplan that are likely to be part of the optimal path. This PPM is then accompanied by either focal search or greedy search that generates the final trajectory. Hence, similar to NA* * Transpath is also not fully differentiable due to the non-differentiable nature of this search process. But we find that the PPM generated from the transformer is a good approximation of the trajectory and can be used as a proxy for the trajectory in gradient- based optimization. In all our experiments, we refer to the PPM generated from the transformer as the output of Transpath to maintain differentiabillity. We use the official implementation of Transpath from Kirilenko al. (2023) and use their checkpoints for evaluation.
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E.4 DIPPER
More recently, DiPPeR Liu al. (224) proposed diffusion- based auth-planning algorithm. DiPPeR formulates path planning as a conditional generation problem where the goal is to generate trajectory given floorplan, start and goal locations. As official code was unavailable, DiPPeR using a U-Net architecture similar to DDPM Ho et al.] (2020) to model the diffusion process directly on the trajectory space instead of the image pixel space. The U-Net takes as input noisy trajectory Xr E _2_x2 floorplan E k#xW start E IR #W and goal E #R and time step t and outputs the denoised trajectory. DiPPeR is trained using standard denoising loss using A generated trajectories as ground truth. At inference, DiPPeR starts from a random noisy trajectory and iteratively denoises it to generate valid trajectory. Because the model has learn "intersections" implicitly, DiPPeR sometimes struggles with complex environments in producing intersecting trajectories that are not valid. To see this, observe the last column in Fig 12 where the DiPPeR run over 5 different seeds. While the first two rows show non-intersecting paths, the remaining bottom three have small intersections, which would not happen for traditional planners. Although DiPPeR is differentiable end-to-end, the denoising process is computationally expensive it requires multiple forward passes through the U-Net get one gradient step. Accelerated sampling methods such as DDIM Song et al. (2020a) can be used to reduce the number of forward passes, and use 50 DDIM steps in experiments. This explicit makes DiPPeR less suitable as a proxy forward operator in our setting.
implemented
Neural A* (history)
Neural A+ (path)
A*
Transpath
Dipper
Figure 12: Comparison of DiPPeR across seeds.
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E.5 COMPARISON OF PLANNERS
compare the behavior of A NA*, TransPath, and DiPPeR from the same start and end on multiple floorplans analyze their performance. We note that all the planners are able plan paths well in these binary (black walls and free spaces). Also, as mentioned E. only the history of Neural A is differentiable.
We
Neural A* (history)
Neural A* (path)
A*
Transpath
Dipper
Figure 13: Comparison of various path planners.
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Reviews:
[{"review_id": "sEcrNRRSA1", "reviewer": "Reviewer_8553", "summary": "The paper proposes CoGuide, a diffusion-based method for spatial inverse problems with the example in the paper being room layout estimation from (simulated) human trajectories. The authors formalize this task as an inverse problem, where the trajectories have been generated by a forward process that applies an operator (in this case a path planning process by a human) on a certain room layout.\nThe goal now is estimate a plausable room layout from a given trajectory. For this, the authors follow the existing framework of Diffusion Posterior Sampling [1], which leverages a trained diffusion model as a data prior and additionally maximizes the likelihood of the observation from the foward process (the trajectory inside the room in this case) during the generation / reverse process, i.e., sampling of the unobserved signal (the room layout). However, for the application to room layout estimation from trajectories, the authors identify the use of differentiable path planner (A*) approximations for the likelihood term as unstable / very sensitive to small sample perturbations.\nTherefore, the authors propose a new method CoGuide that trains encoders for trajectories and floor plans to a common embedding space with contrastive losses and approximates the likelihood term via similarities of embeddings. Additionally, the paper introduces an additional intersection penalty term and explores using Adam instead of SGD for the gradient-based guidance during sampling.\nFinally, an experimental evaluation includes qualitative and quantitative results for different trajectory sparsity levels as well as an ablation study w.r.t. the intersection penalty and the use of Adam.\n\nReferences:\n- [1] Diffusion Posterior Sampling for General Noisy Inverse Problems. ICLR 2023", "questions": "The authors should discuss the representation of floor plans and trajectories as continuous grayscale images, also w.r.t. existing works. Furthermore, I would suggest to address my concerns regarding fair baseline comparisons w.r.t. intersection penalty, Adam, and guidance scale for the Classifier-Free Guidance baselines and others. Furthermore, additional metrics that account for the existence of multiple possible floor plans matching a trajectory would be interesting as well as a comparison with baselines in terms of robustness to measurement noise.\n\nPossibly out of scope for rebuttal: The paper would be much stronger, if it would show the generalization of CoGuide to other applications than the single, synthetic floor plan estimation from human trajectories.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The paper is very well written and easy to follow and understand.\n  - The introduction does a great job in formalizing inverse problems, the application to floor plan estimation from human trajectories, and already summarizes the main idea of the paper in an understandable way.\n  - Preliminaries cover the important existing works to understand the contributions of this paper.\n  - The method section first formulates the problem (in particular why a straightforward use of a differentiable A* approximation in the DPS framework does not work well), also visualizes it in Fig. 1, then describes the approach well and proofs that it is a valid approximation of the true likelihood, and finally visualizes the embedding space in a t-SNE plot to give insight about why their method works intuitively.\n- The method is interesting, general, and to my knowledge sound and novel.\n  - Making use of InfoNCE linking contrastive learning and density estimation to define a likelihood surrogate via similarity in an embedding space learned contrastively seems elegant and also generally applicable to more problems.\n- The qualitative and quantitative evaluation shows strong performance compared to a variety of baselines.\n- The ablation study validates the effectiveness of the wall and trajectory intersection penalty as well as the use of Adam for gradient-based guidance.\n- The appendix provides further qualitative results, implementation details, and information about used path planning algorithms.", "weaknesses": "- Limited evaluation:\n  - Even though the method seems to be general and therefore applicable to other (spatial) inverse problems, the paper is limited to the single task of floor plan estimation from trajectories on a single dataset. The paper could be much stronger, if the authors would provide at least one more application.\n  - For quantitative results, the paper only provides IoU and F1 score as metrics. If I am not mistaken, both metrics do not account for multiple possible floor plans given a trajectory. Would it be possible provide distribution-based metrics similar to FID and possibly check validity in form of no wall-trajectory intersections? At least, these would allow for multiple possible solutions instead of just comparing with the single ground truth sample.\n  - The authors claim that CoGuide is \"more robust than differentiable-planner baselines and guided-diffusion methods\" but they only show this robustness in terms of trajectory sparseness, where CoGuide outperforms all baselines for sparse and moderate trajectory densities. Robustness in terms of the level of noise in the measurements is only analyzed for CoGuide (Fig. 4) but not for baselines.\n- The paper misses to discuss the floor plan and trajectory representations:\n  - I find the representation as grayscale images questionable. To this end, the authors unfortunately do not discuss whether this choice is in line with previous related works and whether there could be any alternatives.\n  - The authors already mention that even though both trajectories and floor plans are binary, planners would have to \"cope with continuous-valued inputs (gray pixels) during the reverse diffusion process\" (line 161). If both are binary, would a discrete diffusion approach be more applicable for example?\n- Concern about comparison with baselines and their selection:\n  - The quantitative and qualitative results are not completely convincing in comparison with the Classifier-Free Guidance baseline. It outperforms CoGuide for dense trajectories and is pretty much on par for moderate trajectory density (Tab. 1) for the two used metrics.\n    - Also, how is the guidance scale tuned? Is it the best performance possible w.r.t. this hyperparameter?\n  - Since both the intersection penalty and Adam are shown to be important for strong performance in their ablations (Tab. 2 and Tab. 3), the question arises whether the baselines also make use of these improvements, which are independent of the actual CoGuide method or not.\n    - To this end, the intersection penalty is a straightforward way of incorporating manual inductive bias specific to floor plan estimation.\n      - If I am not mistaken, it should be possible to use the intersection penalty also in combination with CFG or not? This is important since without the intersection penalty, CFG would mostly outperform CoGuide.\n    - The use of Adam for the gradient-based guidance during the reverse process seems like something completely general to me independent of the application or the rest of the CoGuide method. Is this paper really the first one to do this?\n\n- Minor weaknesses:\n  - In the method section, it could be beneficial to already show some snippet of failure cases from baselines using differentiable A* approximations for the likelihood computation as evidence for the claims and explanations that these are unstable.\n  - The Sec. 3.2 (and the rest of the main paper) does not include any details about the encoder architectures.\n  - It could be interesting to show t-SNE plots for a larger part of the dataset as well.\n  - The GitHub links directly in text in the paragraph about baselines should be avoided. One could just say that you follow the original configurations or at most have them as footnotes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VnYvys1zqV", "reviewer": "Reviewer_7FMZ", "summary": "This paper proposes CoGuide to infer a floorplan from human-walked trajectories. It learns a joint contrastive embedding for trajectory and floorplan pairs and uses the embedding similarity as a likelihood surrogate to guide diffusion sampling. Practical additions include a trajectory–wall intersection penalty and DDIM steps optimized with Adam + cosine LR + early stop. On HouseExpo, CoGuide beats the baselines under sparse–moderate trajectories and is comparable to CFG when trajectories are dense.", "questions": "Any results at higher resolution or with grayscale walls/soft obstacles to test robustness beyond binary maps?\n\nHow does performance change with real trajectories, e.g., human teleop or logs from indoor SLAM?\n\nCan you combine CoGuide with CFG to close the dense-trajectory gap?", "limitations": "", "rating": 6, "confidence": 1, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Clear derivation linking InfoNCE logits to a likelihood ratio and gradients to the likelihood score; switching to unit-norm embeddings yields an equivalent squared-distance form that is smooth for guidance.\nUsing Adam within each step, cosine LR ramp + hard gating, and a trajectory–wall intersection penalty are simple, effective, and empirically ablated.\nThe paper diagnoses instability from 𝐴(⋅), which has large/sensitive Jacobians, as well as non-smooth choices, motivating the embedding approach.", "weaknesses": "Evaluation is entirely synthetic on 64×64 binary maps, which makes the sim2real validity uncertain.\n\nWhile baselines are diverse, CFG wins in dense trajectories. It would be fair to analyze why and whether CFG + contrastive guidance is helpful. Additionally, consider stronger conditional diffusion variants, such as those with modern UNets or latent-diffusion backbones.\n\nOnly Gaussian perturbations to trajectories are tested. Yey, real sensors have biases and drift. A more realistic corruption suite would be convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fpZ5PtJKc4", "reviewer": "Reviewer_rYah", "summary": "This paper introduces CoGuide, a diffusion based approach to solve the inverse problem of reconstructing a floorplan from a user's movement trajectories. A key challenge of the approach is that the forward operator in a floor plan is non-differentiable and non-linear, which destabilizes standard diffusion-based solvers that rely on a likelihood score. CoGuide's key contribution is to bypass this issue by reformulating the likelihood score in a smooth, learned embedding space. This space is trained using a contrastive loss that pulls compatible floorplan-trajectory pairs together while pushing mismatched pairs apart. This creates a stable surrogate likelihood score that can effectively guide the diffusion process, enabling the model to produce more consistent and robust floor plans than baseline methods.", "questions": "Floorpans are downsampled to a resolution of 64x64 in the work's experiment setting. Is this a common practice in solving spatial inverse problem setups? Is there any challenge preventing the approach from being applied to higher resolution inputs?", "limitations": "", "rating": 8, "confidence": 2, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The work is well written and easy to follow. With enough preliminaries, backgrounds and technical details, it easy for readers not familiar with spatial inverse problems to understand the problem setting and the work's contributions.\n2. Each component of the proposed approach is well-motivated, including the learning of the embedding space using contrastive learning, adapting and improving the diffusion models for the spatial inverse problems. The choice of these components are also well supported by ablation study results.\n3. The quantitative evaluation results of the work demonstrates competitive performance of the proposed approach against various baseline models.", "weaknesses": "My biggest concern of the approach is on its comparison against the classifier free guidance approach. The performance gap between CFG and COGUIDE is not significant enough in either sparse, moderate, or dense setting. Is the performance gap a results of randomness or hyper-parameter selection? There're not enough details on the exact instantiation of the baseline CFG model which is also a diffusion-based model and its difference with the proposed COGUIDE approach. More ablation study focusing on the difference between CFG and COGUIDE might help with answer this question.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "H6s5sUYkAw", "reviewer": "Reviewer_cV1B", "summary": "This paper addresses the spatial inverse problem of reconstructing 2D floorplans from human trajectories. The proposed method, CoGuide, is a diffusion-based posterior sampler that avoids the instability of path-planning forward operators by learning a smooth, shared embedding space for floorplans and trajectories using contrastive learning. The learned contrastive similarity score functions as a stable surrogate for the true likelihood, steering the reverse diffusion process toward a compatible layout. CoGuide was evaluated on reconstructing $64\\times64$ floorplan images from synthetic trajectories. Experiments show the method produces more robust and visually consistent floorplans than baselines using differentiable planners (e.g., DPS+Neural A*) and achieves the best overall performance.", "questions": "- The use of an Adam optimizer inside each DDIM guidance step is an unusual modification; what are the theoretical implications of this on the mathematical validity and convergence properties of the reverse diffusion process? Why not use other higher-order ODE solvers? \n\n- How sensitive is the model to the choice of the synthetic data generator? Would performance degrade significantly if the trained models are directly tested with real-world human trajectories?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper clearly motivates its approach by demonstrating that the core path-planning forward operator is unstable, non-differentiable, and non-smooth.\n\n- The central proposal to learn a smooth likelihood surrogate in a contrastive embedding space is a novel and well-reasoned solution to bypass the problematic forward operator of the studied problem.\n\n- The experiments are thorough, comparing CoGuide against six relevant baselines (including DPS+planners and CFG) and showing superior results in sparse and moderate data regimes.", "weaknesses": "- The method might suffer from a significant \"sim-to-real\" gap, as the encoders are trained exclusively on synthetic $A^*$ (shortest-path) trajectories and on empty floorplans lacking real-world obstacles like furniture. The authors should discuss how the approach would generalize to more complicated real-world problem settings.\n\n- There is no explanation for why the CFG baseline outperforms CoGuide in dense trajectory settings.\n\n- The method's practical applicability is questionable due to scalability, as it is only demonstrated on small $64\\times64$ images, a limitation the authors attribute to the poor scaling of path planners.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)