Grounded spatial reasoning about indoor scenes rests on a geometric reconstruction that the multimodal system does not itself produce. Feed-forward multi-view reconstruction models operate under a fixed view budget, making frame selection a critical upstream decision. We show that under this setting the conventional preference for sharp frames can be actively harmful. Across nine handheld room scans, selecting the sharpest 16 of 200 candidate frames is the worst strategy we test, reaching 48% room dimension error where uniform temporal sampling reaches 3%. We trace this failure to a coupling between image quality and camera motion: sharp frames tend to occur during slow motion and therefore concentrate the selection in a narrow angular region, leaving 192∘ of the room unobserved in the case we illustrate and over 300∘ in the worst cells. A 2×2 factorial experiment that independently manipulates viewpoint spread and per-frame sharpness shows that spread has roughly three times the main effect of quality, and that blurry but well spread selections outperform sharp but clustered selections in 8 of 9 captures. The advantage also diminishes as the frame budget increases and angular coverage saturates, consistent with this mechanism. Finally, we ask whether the failure is detectable at inference: the reconstruction model's multi-view consistency score increases with reconstruction error, while a vision-language model given the same frames reports confidence unrelated to its own spatial estimation error. Under small view budgets, viewpoint coverage is a more important constraint than per-frame image quality, and neither uncertainty signal available at inference reveals this upstream failure.
Figures & tables
capture
measured (m)
dense reference (m)
relative error
bedroom
3.39×3.39
3.94×3.63
11.8%
work_office
3.90×2.62
4.22×2.83
8.1%
home_office
4.72×3.72
4.04×3.41
11.4%
Table 1: Hand measured dimensions against the dense 200-frame reference, for the three captures we could physically access. Mean absolute error is 10% with no consistent direction: the reference overestimates both dimensions of bedroom and work_office and underestimates both dimensions of home_office. Room shape is nonetheless recovered, with the square room reconstructed as square. Note also that these three captures have the best self-consistency in the set (0.3 to 1.1%, Section 3.3 ) while showing 8 to 12% accuracy error, so internal agreement between dense reconstructions does not predict agreement with reality.
Figure 1: Sharpness ranked selection against uniform sampling on one capture ( K=16 ). Top: viewing directions of selected frames (colour) over the yaw distribution of all 200 candidates (grey). Bottom: reconstructed point cloud from above, with the fitted rectangle (solid) and the dense 200-frame reference (dashed), registered to a common centre. Rectangles use 5 to 95 percentile extents, so sparse peripheral points fall outside them.
Relative error
Degenerate
high quality
low quality
high quality
low quality
spread
0.110±0.077
0.154±0.131
0.000
0.025
clustered
0.324±0.152
0.435±0.166
0.086
0.395
Table 2: 2×2 factorial at K=16 , marginalised over view ordering. Means and standard deviations over 81 reconstructions per cell from nine captures; inference treats the capture as the unit ( n=9 ).
Figure 2: The factorial contrast on one capture: clustered-high against spread-high, identical mean sharpness (0.22), differing only in spread. Same conventions as Figure 1 .
K
8
16
32
48
clustered error
0.421
0.316
0.268
0.192
spread error
0.160
0.104
0.078
0.087
gap
0.262
0.212
0.190
0.105
coverage achieved (spread)
0.222
0.444
0.889
1.000
degeneracy, clustered-low
0.500
0.417
0.167
0.000
Table 3: Budget sweep over the four full sweep captures available when it was run, three seeds each, so twelve reconstructions per cell. K=12 and K=24 are omitted for space and interpolate smoothly.
relative error
multi-view confidence
spread, high quality
0.110
0.444
spread, low quality
0.154
0.363
clustered, high quality
0.324
0.669
clustered, low quality
0.435
0.674
Table 4: Multi-view consistency score against error, means over 81 reconstructions per cell. The ordering is inverted throughout.
Figure 3: Neither downstream signal tracks the upstream failure. (a) Per capture and strategy, reconstruction error against the VLM’s room-dimension error: the selections that break the geometry are not the ones the VLM gets wrong. (b) Error distribution at each of the four confidence values the model actually reports, with individual queries jittered over the boxes. Of 189 queries, 131 return a confidence of exactly 0.30 and the 0.40 bin contains only eight, so the outer bins should not be over-read. Colour denotes capture. Section A gives the query text.