FreeOcc: Decoupling Ego-Motion for Efficient 4D Occupancy Forecasting via Continuous Flow Matching

Zeping Zhang; Zhuoya Zhao; Samy Metari; Robert Laganière

cs.CVNov 5, 2026

FreeOcc: Decoupling Ego-Motion for Efficient 4D Occupancy Forecasting via Continuous Flow Matching

Authors: Zeping Zhang, Zhuoya Zhao, Samy Metari, Robert Laganière

Organizations: University of Ottawa · National Research Council Canada

Abstract

Current 4D occupancy world models use heavy cross-attention or deep recurrence to disentangle ego-motion from scene dynamics. This requires significant computational capacity. However, this effort is largely redundant since the ego-vehicle's intended future motion is provided by the downstream motion planner. We present FreeOcc, a lightweight continuous-time world model that introduces explicit $SE(2)$ geometric decoupling into a conditional flow-matching framework. By mathematically factoring out self-motion, we create a purely dynamic feature space. This space supports attention-free temporal fusion and allows physics-constrained ODEs to operate strictly on residual agent kinematics. To maximize deployment efficiency, FreeOcc introduces an uncertainty-aware blending mechanism. This routes well-observed voxels to a single-pass decoder, reserving the ODE solver exclusively for ambiguous, high-uncertainty regions. Requiring only 28.3M parameters-roughly 40% of comparable models-our optimal ensemble mode (FreeOcc-E) runs at 27 FPS. On nuScenes, FreeOcc achieves state-of-the-art forecasting performance with a 0.47 m Near Field Chamfer Distance (NFCD) and 32.1% mIoU. Furthermore, the model transfers zero-shot to SemanticKITTI. It also proves highly robust, retaining 92.8% of its ideal mIoU under significant sensor packet loss and timestamp jitter.

Figures & tables

Figure 1: Overview of FreeOcc. Previous sweeps are encoded into BEV, fused by a continuous-time temporal aggregator, propagated to future ego frames by $\mathrm{SE}(2)$ warping, and decoded by the Discriminative Occupancy Decoder plus the Physics-Aware Continuous Flow Matching. The final prediction blends both paths using decoder uncertainty.

Figure 2: Ego-aware future propagator. At each horizon, the warped BEV—obtained via SE(2) ego-motion alignment—is explicitly added to the residual scene dynamics predicted by a ConvGRU and a residual projector.

Method	1.0s ( $\tau{=}2$ )			3.0s ( $\tau{=}6$ )
Method	NFCD	L1	AbsRel	NFCD	L1	AbsRel
4D-Occ † Khurana et al. (2023)	1.41	1.40	10.37	1.40	1.71	13.48
Copilot4D † Zhang et al. (2024)	0.36	1.30	8.58	0.58	1.51	10.38
UnO † Agro et al. (2024)	–	–	–	0.89	1.15	8.50
ViDAR † Yang et al. (2024)	0.85	1.55	11.50	1.18	1.82	14.20
FreeOcc-D (ours)	0.48	1.25	8.15	0.63	1.35	9.20

Table 1: 3D Geometry Forecasting on nuScenes. Evaluated via Near Field Chamfer Distance (NFCD, m)

\downarrow

, L1 Depth Error (m)

\downarrow

, and Absolute Relative Error (AbsRel, %)

\downarrow

Method	Forecasting Horizon mIoU (%)						Average Metrics (%)
Method	$\tau{=}1$	$\tau{=}2$	$\tau{=}3$	$\tau{=}4$	$\tau{=}5$	$\tau{=}6$	mIoU	Prec.	Rec.	F1
Static Copy	31.5	22.1	14.8	10.5	8.8	7.5	15.9	34.0	23.5	27.8
4D-Occ ∗ Khurana et al. (2023)	35.8	27.5	22.4	19.0	16.5	14.2	22.6	39.1	31.8	35.1
OccWorld Zheng et al. (2024)	39.7	34.2	27.9	26.4	23.6	21.6	28.9	50.5	40.2	44.8
ViDAR ∗ Yang et al. (2024)	30.5	24.2	20.5	17.8	15.9	14.3	20.5	39.5	29.8	34.1
FreeOcc-D (ours)	40.8	34.3	29.1	25.1	21.8	19.2	28.4	41.3	49.4	44.9

Table 2: 4D Occupancy Forecasting on nuScenes. Evaluated via Class-Agnostic 3D Voxel mIoU (%)

\uparrow

Method	#Params	Latency (ms)	FPS
4D-Occ Khurana et al. (2023)	8.59M	73	13.7
OccWorld Zheng et al. (2024)	72.4M	56	17.8
ViDAR Yang et al. (2024)	69.1M	189	5.3
FreeOcc-D (ours)	20.7M	22	45.4
FreeOcc-G (ours)	7.5M	168	6.0
FreeOcc-E (ours)	28.3M	37	27.0

Table 3: Efficiency comparison on nuScenes. We evaluate the parameter count and inference speed of our three variants: D (Decoder-only), G (Generation-only), and E (Ensemble) against representative baselines. All measurements are conducted on a single NVIDIA RTX 4090.

Method	Short-term ( $\tau\leq 3$ )			Long-term ( $\tau>3$ )
Method	mIoU	Precision	Recall	mIoU	Precision	Recall
Static Copy	14.5	30.2	22.1	7.5	22.5	13.0
4D-Occ Khurana et al. (2023)	15.2	31.8	24.4	8.9	24.1	15.2
OccWorld Zheng et al. (2024)	18.5	38.1	30.1	12.3	31.5	22.1
ViDAR Yang et al. (2024)	6.5	12.4	8.3	2.3	1.5	2.2
FreeOcc-D (ours)	22.0	42.5	39.8	15.5	34.1	33.2

Table 4: Zero-shot transfer from nuScenes to SemanticKITTI (no fine-tuning). We report metrics across short-term and long-term horizons to assess structural preservation under distribution shift.

Figure 3: 4D occupancy forecasting on nuScenes(sample 1720) in BEV and 3D views across six horizons. FreeOcc-E is compared against OccWorld and ViDAR; more qualitative examples are given in Appendix D .

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Condition	mIoU $\uparrow$	Prec. $\uparrow$	Rec. $\uparrow$	F1 $\uparrow$
4D-Occ Khurana et al. [2023]	Ideal	22.6	39.1	31.8	35.1
4D-Occ Khurana et al. [2023]	Imperfect	14.2	30.5	21.0	24.9
OccWorld Zheng et al. [2024]	Ideal	28.9	50.5	40.2	44.8
OccWorld Zheng et al. [2024]	Imperfect	19.0	38.5	28.5	32.8
ViDAR Yang et al. [2024]	Ideal	20.5	39.5	29.8	34.1
ViDAR Yang et al. [2024]	Imperfect	12.8	28.2	19.5	23.1

Appendix

Table 5: Robustness to sensor temporal imperfections on nuScenes. The imperfect setting adds severe timestamp jitter and 30% history-frame dropout. All metrics are voxel-based.

Generation Path Ablations
Variant	mIoU $\uparrow$	Precision $\uparrow$	F1 $\uparrow$
FreeOcc-E (full)	32.1	54.6	49.6
w/o Flow Matching (FreeOcc-D)	28.4	41.3	44.9
w/o Decoder (FreeOcc-G)	30.3	52.5	47.4
Max blending	31.2	53.4	48.4
Mean blending	30.5	52.6	47.5

Appendix

Table 6: Ablation study on nuScenes validation. Metrics are voxel-based. The results demonstrate that explicit geometric decoupling and physics-aware regularization are the primary drivers of forecasting quality.

Figure 4: BEV visualization of 4D occupancy forecasting (additional scene, sample 0970). Columns span $\tau{=}1$ to $\tau{=}6$ , comparing FreeOcc against OccWorld and ViDAR on a dense urban scene with multiple interacting vehicles. FreeOcc-E preserves scene layout and object extent more faithfully over the full 3.0 s horizon, whereas OccWorld and ViDAR drift more noticeably after $\tau{=}3$ .

Figure 5: Temporal evolution of GT vs. FreeOcc-E (nuScenes sample 1730). Top row: ground-truth occupancy at $\tau{=}2$ (1.0 s), $\tau{=}4$ (2.0 s), and $\tau{=}6$ (3.0 s), each shown as BEV (left) and 3D voxel rendering (right). Bottom row: corresponding FreeOcc-E predictions. Agreement is strongest at short horizons. By $\tau{=}6$ , the main discrepancies appear in the far field and around dynamic actors, while large static structures remain well preserved.

Figure 6: Long-horizon comparison at $\tau{=}6$ (3.0 s, nuScenes sample 3380). Ground Truth, OccWorld, FreeOcc-D, and FreeOcc-E are shown in paired BEV and 3D views. At 3.0 s, OccWorld exhibits boundary blur and noise around moving objects. FreeOcc-D slightly over-predicts occupancy, whereas FreeOcc-E preserves sharper boundaries and cleaner free space.

Panel of Figure 7 — Figure 7: 3D voxel-level uncertainty across prediction horizons (scene-0274, $\tau{=}1$ to $\tau{=}6$ ). Each panel renders occupied voxels coloured by the decoder’s predicted aleatoric uncertainty $\hat{s}_{v}$ ; cool tones indicate low uncertainty and warm tones indicate high. At short horizons, uncertainty is concentrated near dynamic actors and occupancy boundaries. As the horizon increases, uncertainty expands into the far field and intensifies around moving objects. By $\tau{=}6$ (3.0 s), most remaining structures are assigned substantially higher uncertainty. This behaviour is consistent with the ensemble design in Section 3.7 , which assigns more weight to the ODE path where decoder confidence weakens.

Figure 8: Noise suppression analysis (nuScenes sample 1290). Paired BEV and 3D views highlighting hallucinated occupancy at long prediction horizons between FreeOcc-D and FreeOcc-E. FreeOcc-E clears vacated space more effectively while preserving plausible future object positions.

Figure 9: BEV uncertainty evolution (nuScenes scene-0220, $\tau{=}1$ to $\tau{=}6$ ). Each panel shows the BEV occupancy coloured by the predicted aleatoric uncertainty $\hat{s}_{v}$ at horizon $\tau$ . Cool colours (cyan/green) indicate low uncertainty; warm colours (orange/red) indicate high uncertainty. Uncertainty increases with horizon and concentrates on dynamic objects and far-field voxels. Similar behaviour appears in Figure 7 .

Figure 10: Zero-shot SemanticKITTI predictions (sample 2070). BEV and 3D occupancy predictions from a nuScenes-trained model evaluated on SemanticKITTI, comparing FreeOcc-E against OccWorld and ViDAR. Despite domain differences (64-beam LiDAR, different driving context, no fine-tuning), FreeOcc-E preserves road-level structure and major obstacles. The denser 64-beam input provides richer geometric detail than nuScenes’s 32-beam data, and FreeOcc adapts without retraining, an advantage of operating on raw 3D geometry rather than appearance-dependent features.

Figure 11: Additional zero-shot SemanticKITTI scene (sample 1080) and noise suppression analysis. Top: 3D occupancy predictions at the maximum forecasting horizon ( $\tau=6$ , 3.0s), comparing our model variants (FreeOcc-D, -G, -E) against Ground Truth. Bottom: BEV temporal evolution at $\tau=3$ and $\tau=6$ . Road geometry, vegetation boundaries, and building walls are consistently captured, reinforcing the cross-domain robustness reported in Table 4 .

Figure 12: Robustness on nuScenes ( $\tau=4$ , 2.0s, sample 2500). Occupancy predictions under ideal and imperfect sensor conditions. FreeOcc-E retains its predictions under severe temporal perturbations, while OccWorld and ViDAR break into fragments and hallucinated clusters when temporal input is degraded.

Figure 13: Cross-domain robustness on SemanticKITTI ( $\tau=4$ , 2.0s, sample 2090). Zero-shot predictions under ideal (top two rows) and imperfect (bottom two rows) sensor conditions. The cross-domain robustness gap compounds both the sensor-domain shift (nuScenes $\to$ SemanticKITTI) and the temporal perturbation, yet FreeOcc-E retains recognisable road and object geometry in both cases, while OccWorld and ViDAR suffer severe structural collapse.

Figure 14: Robustness on nuScenes at $\tau{=}6$ (3.0 s, sample 1600). Long-horizon predictions under ideal (top) and imperfect (bottom) sensor conditions. At the maximum forecasting horizon, OccWorld and ViDAR dissolve into discontiguous fragments, whereas FreeOcc-E preserves the road layout and principal object silhouettes. The gap between ideal and imperfect rows is noticeably smaller for FreeOcc-E than for OccWorld or ViDAR, consistent with the quantitative retention rate in Table 5 .

Figure 15: Cross-domain robustness on SemanticKITTI at $\tau{=}6$ (3.0 s, sample 2090). The most challenging evaluation setting: simultaneous cross-domain transfer, long-horizon prediction, and degraded sensor input. FreeOcc-E still recovers the overall road layout and major object boundaries, whereas OccWorld and ViDAR suffer near-complete structural collapse.

Figure 16: Rain scene overview (nuScenes scene-0441). Left: front camera image showing wet road, reflections, and rain droplets. Right: LiDAR BEV coloured by height; the intersection geometry and parked vehicles are clearly visible despite rain-induced noise in the point cloud.

Method	Mean (ms)	Worst (ms)	FPS (mean)
OccWorld (ideal, ref.)	56.0	—	17.8
FreeOcc-D (rain)	22.1	23.8	45.2
FreeOcc-E (rain)	37.3	39.1	26.8

Appendix

Table 7: Inference latency on rain sequence (nuScenes scene-0441, 40 keyframes). Both FreeOcc variants maintain stable latency indistinguishable from dry-weather conditions. FreeOcc-E’s worst-case is still 1.4

\times

faster than our OccWorld run under ideal conditions.

Traj. error @3 s (L2)	0.0 m	0.3 m	0.5 m	1.0 m	2.0 m	4.0 m
FreeOcc-E mIoU	32.1	31.8	31.5	31.1	30.2	28.6

Appendix

Table 8: FreeOcc-E mIoU under injected future-trajectory error on nuScenes.

Trajectory	mIoU	NFCD@3s
Recorded route (paper setting)	32.1	0.47
Self-predicted from history	31.2	0.51
OccWorld (no external traj.)	28.9	—

Appendix

Table 9: FreeOcc-E with a self-predicted trajectory (no future information).

Method	mIoU	Prec.	Rec.	F1	NFCD@3s
Static Copy	15.9	34.0	23.5	27.8	1.11
Warp+Fuse	20.4	35.3	33.1	34.2	1.04
Warp+Fuse+FOV	20.8	36.5	33.4	34.9	0.98
FreeOcc-D	28.4	41.3	49.4	44.9	0.63
FreeOcc-E	32.1	54.6	45.5	49.6	0.47

Appendix

Table 10: Information-matched warp baselines versus FreeOcc on nuScenes.

Method	Static mIoU	Dynamic mIoU	Overall	Dyn. margin vs OccWorld
Warp+Fuse+FOV	26.8	4.2	20.8	$-10.3$
OccWorld	37.2	14.5	28.9	—
FreeOcc-E	37.5	19.6	32.1	$+5.1$

Appendix

Table 11: Static and dynamic mIoU on nuScenes.

Method	Modality	mIoU	NFCD@3s	FPS
SparseWorld-TC	Camera	23.7	0.82	9.8
FreeOcc-Cam (ours)	Camera	25.6	0.69	19.4
FreeOcc-E (ours)	LiDAR	30.2	0.47	27.0

Appendix

Table 12: Comparison with SparseWorld-TC on our metrics (

\{1,2,3\}

s horizons).

Configuration	Modality	mIoU
FreeOcc (full)	LiDAR	32.1
FreeOcc-Cam	Camera	26.5
Camera baseline (no warp/CFM/ensemble)	Camera	21.9

Appendix

Table 13: Isolating the modality from the forecasting modules (6-horizon average on nuScenes).

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